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A digital twin-driven approach for the assembly-commissioning of high precision products
Robotics and Computer Integrated Manufacturing 61 (2020) 101839
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Robotics and Computer Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim
Full length Article
A digital twin-driven approach for the assembly-commissioning of high precision products
T
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Sun Xuemina, Bao Jinsonga, , Li Jiea, Zhang Yimingb, Liu Shimina, Zhou Bina a b
College of Mechanical Engineering, Donghua University, Shanghai 201620, China College of Literature, Science and the Arts, The University of Michigan, Ann Arbor, MI 48109, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Digital twin Multidisciplinary coupling High precision Assembly-commissioning Data fusion Interoperability
High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.
1. Introduction As one of the critical elements, HPPs are mainly used in aerospace, marine, chemical and other fields. The quality of the machinery and assembly is one of the key factors determining the quality of the final product. With the continuous improvement of the machining precision of high precision CNC machine tools, the machining accuracy of product parts has reached a relatively high level. HPPs have complicated assembly schedules and high precision requirements, and need to reach micron or even sub-micron scale in the process of assembly. The process is further complicated by the interactions between electromagnetic, physical, hydraulic, structural characteristics and the multidisciplinary problems in the domains of machinery, electronics and hydraulics. Furthermore, the error in the assembly quality consistency is required to be less than 1%. Therefore, research on the assembly process of HPPs has become an effective way to improve production quality. For a long time, the assembly process for HPPs have always been based on manual experience. Only high skill operators are capable of these challenging assembly operations. However, it is still difficult to guarantee the consistency and accuracy of the assembly, i.e. the onetime pass rate of the product is relatively low. In addition, the rework and re-assembly of unqualified products increased the labor intensity of workers, which affects the efficiency of the assembly to a certain extent. With the increasing application of HPPs and the growing demand for ⁎
technology, how to improve the quality and efficiency of assembly has become an urgent problem. Therefore, this paper proposes a digital twin-driven assembly-commissioning approach for HPPs, aiming at improving both assembly quality and efficiency. The rest of this paper is organized as follows. Section 2 provides a brief review of existing digital assembly technologies. Section 3 provides a theoretical framework for digital twin-driven assembly-commissioning and the introduction of the virtual and physical assembly space. Section 4 introduces the operation method of digital twin-driven assembly-commissioning, including the method of modeling the total factor information model of digital twin, the fusion of twin data, the interoperability method between models, the data pre-parable predictability of twin data, and the assembly-commissioning process optimization. In Section 5, a case study of the assembly process of a highprecision electro-hydraulic servo valve is used to verify the feasibility of the method. The last section is the conclusion of this research. 2. Literature review The rapid development of high-tech technologies (i.e. artificial intelligence, computer simulation, industrial big data) have promoted the digitalisation of assembly. The development process of digital assembly can be divided into three stages as shown in Fig. 1. The first phase (around Year 2000), the virtual assembly technology
Corresponding author. E-mail address: [email protected] (J. Bao).
https://doi.org/10.1016/j.rcim.2019.101839 Received 29 March 2019; Received in revised form 8 July 2019; Accepted 11 July 2019 0736-5845/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Digital assembly development stage.
emphasizing on large flexible docking assembly through real-time measurement. Ke [18–22] studied key issues such as position accuracy compensation and position control of aircraft assembly docking. Shao [23] presented the virtual assembly architecture and assembly process simulation based on physical properties. Liu and Zhang [24,25] proposed a virtual assembly technique based on precision and physical properties to simulate various problems in the assembly site in real time from the perspective of process, precision and physical properties. The quasi-physical virtual assembly is based on the physical properties of the product. It is found that quasi-physical virtual assembly is mainly applied in the assembly of aircraft for the interaction of dimensional accuracy data between physical and virtual spaces. The quasi-physical virtual assembly achieves the initial integration of virtual and physical data, so that the assembly accuracy of the product can meet the requirements. However, this technology is not capable of databased product performance prediction and process dynamic optimization. Therefore, it cannot be used in the assembly of HPPs which involves the multidisciplinary coupling issue.
development period, focusing on process design and verification. The second phase (around Year 2010), the development phase of the quasi-physical virtual assembly technology, focusing on the assembly site. The third stage (around Year 2020), the development period of digital twin assembly technology, focusing on integration of assembly and commissioning. 2.1. Virtual assembly technology Virtual assembly refers to the use of virtual reality technology, computer graphics, artificial intelligence technology and simulation technology to construct virtual environment and product virtual model, for the purpose of interactive analysis and simulation of assembly [1]. The Washington State University and the National Institute of Standards and Technology (NIST) [2] jointly developed the VADE system, which used virtual reality technology to import CAD models into the system for virtual assembly operations. The Sandia National Laboratory [3] developed a set of Archimedes virtual assembly systems for the production of interactive assembly planning that optimize and inspect assembly process. Tan [4,5] studied the hybrid modeling, semantic-oriented expression and assembly sequence deviation transfer model of virtual products. Liu and Ning [6–9] presented the assembly properties of flexible products, flexible cables, accumulation of assembly deviations and accuracy prediction. Tang [10–12] studied flexible assembly for key assembly characteristics in aircraft component assembly. Wu and Fan [13,14] proposed the collaborative product assembly operation simulation of complex products in virtual environments, assembly modeling for operational process planning and virtual human modeling for interactive assembly and disassembly operations. At present, the virtual assembly technology has been applied, to improve the assembly quality and efficiency of the product. However, the majority of the research focuses on the assembly of large-scale products. There are few studies on the assembly of HPPs. Because the virtual assembly focuses on the digital simulation in the virtual environment, it cannot deal with the external interference factors in the physical assembly process. It is impossible to achieve assembly accuracy requirements for HPPs.
2.3. Digital twin assembly The digital twin assembly is in the second stage of the virtualphysical fusion assembly. Based on the virtual assembly information model and the quantitative calculation of assembly quality, the virtualphysical interaction, data fusion, decision analysis and iterative optimization in the whole assembly process are realized using twin data of assembly context. The concept of digital twin was first proposed by Professor Grieves [26,27]in the product lifecycle management class. Ferguson [28] used Siemens STAR CCM + and digital twin to develop an accurate virtual model, and it is also used in product development for accurately reproducing physical and performance characteristics of products. Tao [29–31] established the digital twin workshop (DTW) model, expounding the system composition, operation mechanism and key technologies of the digital twin workshop to guide the construction of a new generation of digital workshops. In order to solve the problem of lack of interaction between physical space and virtual space in manufacturing, Bao [32] proposed the method integrates product, process, operation digital twin as well as the interoperability mode between the digital twin. Oyekan [33] uses of a virtual reality digital twin of a physical layout as a mechanism to understand human reactions to both predictable and unpredictable robot motions. Lu [34] illustrates a generic architecture for cloud-based manufacturing equipment based on technologies such as digital twin and big data analytics. Petković [35] proposes a ToM based human intention estimation algorithm for flexible robotized warehouses, in order to demonstrate the scalability of the approach to larger ware-houses. They propose to use virtual reality digital ware-house twins in order to simulate worker behavior in
2.2. Quasi-physical virtual assembly Quasi-physical virtual assembly [15] is the initial stage of the virtual-real fusion assembly. This technology is mainly used for the development of assembly process, assembly information model, model evolution law and visualization method. For example, the quasi-physical assembly has been widely used in the field of flexible docking of large components such as airplanes. Jafar and Kayani [16,17] took the lead in putting forward the concept of Measurement Assisted Assembly, 2
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commissioning is established. As shown in Fig. 2, the framework structure includes physical assembly space, virtual assembly space and digital twin model. The main content of the research is the operation method of digital twin assembly, it includes: A. Construction method of assembly total factor information model based on the digital twin. B. Twin data fusion method for the assembly process, and interoperability method between digital twin models. C. Twin data-driven assembliability prediction method, and assembly process dynamic optimization method.
reality. In order to achieve the real-time data feedback of digital twin, the fibre channel (FC) switch based on field programmable gate array (FPGA) is designed and implemented due to its high speed, low latency, and high- performance transmission capacities by Tao [36]. Nikolakis [37] focuses on a Cyber-Physical System (CPS) for enabling humanrobot collaboration based on real-time evaluation of safety distance and a closed-loop control for triggering collision preventive actions. As one of the core technologies in CPS, there is a great potential to apply digital twin into human-robot collaboration studies. In the field of manufacturing, the application of digital twin is mainly in the construction of digital workshops, and the majority of the research is based on theoretical research such as the model framework of digital twin without physical application in industries. Some of the literature has carried out preliminary explorations on digital twin assembly technology. Liu [38] proposed a digital twin assembly workshop architecture covering the physical assembly workshop, virtual assembly workshop, workshop twin data and assembly workshop service system in the field of aerospace assembly, and studied the real-time perception of physical assembly workshop data. There are three key technologies such as acquisition, virtual assembly shop modeling and simulation operation technology, and assembly workshop production control. Liu [39-41] proposed a complex product assembly process driven by digital twin. Taking satellite product assembly as the object, using augmented reality technology to assist assembly, the assembly process design method based on a 3D model, a 3D assembly process demonstration model and lightweight display technology were studied. In conclusion, the digital twin assembly inherits the advantages of virtual assembly and quasi-physical virtual assembly, and combines the rapid development of advanced technologies such as computing technology, artificial intelligence and data measurement to realize the virtual space and physics of the assembly process. The digital twin assembly technology meets the dimensional accuracy requirements and dimensional performance requirements in the assembly process of HPPs, and provides a new method for quickly breaking through the assembly problems of HPPs.
3.1. Physical assembly space The physical entities in the physical assembly space mainly include: people, equipment, components and products. Process generated from the simulation in the virtual space can be transferred to the physical assembly space for the practical production. The physical assembly space under digital twin has the following advantages compared with traditional physical assembly space: (1) A series of sensors and digital measurement tools have been used for real-time data acquisition and dynamic tracking in the assembly process; (2) The utilisation of digital and intelligent equipment helps the improvement of production efficiency, quality, as well as the collection and storage of real-time data. (3) The quality control of the assembly process adopts intelligent management; (4) An intelligent algorithm is developed and used for dynamic optimisation of the assembly processes. (5) Interactive fusion between multiple heterogeneous elements of the shop floor is achieved. 3.2. Virtual assembly space The digital twin model of the virtual assembly space is a digital mapping of the physical space entity model, integrating and blending features such as geometry, physics, behavior, and rules. Three digital twin models are built in the virtual assembly shop according to the assembly process: assembly component digital twin model, process digital twin model and assembly performance digital twin model. In the horizontal directions, the three digital twin models establish data interaction and fusion between the physical entity of the component and the 3D geometric model of the component, data interaction and fusion between the physical assembly process and the virtual assembly
3. The theoretical framework for digital twin-driven assemblycommissioning The research aims to apply the digital twin technology in the assembly process of HPPs. Based on the virtual-physical mapping relationship between physical assembly space and virtual assembly space, the theoretical framework of digital twin-driven assembly-
Fig. 2. The framework of digital twin-driven assembly-commissioning. 3
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Fig. 3. Virtual-physical dimension deviation calculation.
Step3: Through special high-precision indirect measuring equipment, the internal features of the cavity are obtained; Step4: For the measured indirect data, the closed contour data information is generated by the mathematical model analysis, and finally integrated into the point cloud data.
simulation, and between the physical product and the product performance model. In the vertical direction, the data interaction and interoperability between the assembly component digital twin model, the process digital twin model and the assembly performance digital twin model are performed. In the actual assembly process, the collected realtime assembly data and analysis results are used to realize the dynamic optimization commissioning of the assembly process based on the intelligent algorithm.
(3) Fusion of the theoretical model and the physical model The key assembly-commissioning features are used as the registration points to realize the fusion of the theoretical model and the physical model, and the digital twin model is constructed. The assemblycommissioning data fusion and deviation calculation of the manufacturing deviation are shown in Fig. 3.
4. The operation method for digital twin-driven assemblycommissioning 4.1. Assembly total factor information model based on digital twin Digital twin refers to a comprehensive physical and functional description of component, product, or system. Development of the digital twin model of assembly-commissioning part, assembly-commissioning process and assembly-commissioning performance is essential for subsequent implementation of the digital twin assembly-commissioning operation process.
4.1.2. Assembly-commissioning process digital twin model The digital twin model for the assembly-commissioning process mainly includes dynamic iterative optimization of the process-parameters and real-time iterative optimization of the assembly-commissioning processing. The specific iterative process is shown in Fig. 4. (1) Process-parameters optimization
4.1.1. Assembly-commissioning part digital twin model The assembly-commissioning part digital twin model is a fusion of theoretical and physical models.
The process refers to planning and determining the assembly route and process-parameters from the part to the assembly under the control of a certain target and the actual assembly resources. The assembly process of the traditional virtual assembly is to use Computer Aided Process Planning to receive product design information from the CAD system, and rely on the process knowledge base to assist the process designer to make decisions and generate assembly process-parameters. Although the method improves the process generation efficiency to a certain extent, the generated assembly process is static and cannot cope with the influence of disturbance factors occurring in the physical assembly process-parameters, and thus cannot make real-time dynamic response according to the processing deviation, which affects the final product quality. Based on the traditional process generation, combined with real-time data acquisition technology and intelligent algorithms, real-time dynamic commissioning and optimization of process-parameters can be realized. Its process-parameters information includes product information, process information, and resource information.
(1) Theoretical model The part theory model can be built using the Model Based Definition (MBD) based modeling techniques. The established part MBD model involves geometric information and non-geometric information. Nongeometric information mainly includes material properties, 3D annotation information, and engineering annotations, where engineering annotations primarily describe the product definition information that must be provided by the process plan. (2) Physical model The physical model can be built by point cloud scanning. The research on measured data is relatively mature, especially the assemblycommissioning feature recognition, modeling and registration of sparse large amounts of data. For an assembly-commissioning part, the traditional point cloud scanning method is to scan the surface of the workpiece to be tested without any difference, such as assembly-commissioning features, reference features and other key parts, resulting in huge data volume and data redundancy. The reverse modeling technology of parts considering key assembly-commissioning features, realtime acquisition of point cloud data of assembly-commissioning parts, the specific steps are as follows:
(2) Optimization of assembly-commissioning processing The real-time iterative optimization of the assembly-commissioning processing reflects the data interaction process between the physical space and the virtual space. The physical space assembly-commissioning processing is assembled by receiving process-parameters optimized from the assembly-commissioning process. Real-time acquisition of assembly-commissioning process data by sensors and other devices, uploading the collected data to the virtual space, the virtual assemblycommissioning processing updates the state according to the real-time state of the physical workshop, and compares the physical assemblycommissioning data of the physical space with the predefined theoretical data. Determine whether the comparison error exceeds the specified threshold, optimize with an intelligent algorithm, and form an optimized assembly-commissioning processing file to perform virtual process-parameters simulation.
Step1: Determining assembly-commissioning part assembly-commissioning features, datum features, boundary features, etc.; Step2: According to the geometric shape of the parts, the key assembly-commissioning features, determine the motion trajectory of the part laser scanning system, the rotation of the turntable, the scanning frequency of the special measuring equipment, and obtain the high-precision measurement of the 3D feature top point cloud; 4
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Fig. 4. Iterative optimization of process-parameters and assembly-commissioning processing.
finally determine the main parameters affecting the performance indexes of high-precision complex products. In addition, although each dynamic analysis model reflects the performance of different aspects of high-precision assembly components, they affect each other and restrict each other. In this paper, the statistical analysis of performance indicators is designed and utilised to study the correlation method, and the linear fitting degree of each index is analyzed. In high-precision assembly assemblies, small differences in geometry parameters and material properties can be amplified in assembly performance, resulting in product failure. Therefore, it is necessary to consider the geometric deviation and its transfer relationship in the original static characteristic equation, and then deduct the theoretical formula and enrich the theoretical model to make it closer to the physical model.
4.1.3. Assembly performance digital twin model The established assembly-commissioning performance digital twin model is mainly used for assembly-commissioning quality assessment, including appearance quality, assembly error, dimensional accuracy, and overall performance. Taking a high-precision electro-hydraulic servo valve as an example, it is necessary to evaluate not only the dimensional accuracy under the pose constraint but also the multi-disciplinary performance requirements regarding dimensional parameters after assembly. As shown in Fig. 5, the correlation analysis method is used to determine the gray relational order of the geometrical, physical, electromagnetic, and hydraulic factors of high-precision assembly parts for each performance index, comprehensively analyze the different gray correlation sequences obtained by different data normalization methods, and associate multiple gray correlations. The geometric, physical, electromagnetic and hydraulic parameters ranked in the front are the main geometric parameters affecting the performance index, to
Fig. 5. Performance digital twin models based on correlation analysis. 5
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Fig. 6. Twin data processing and fusion.
constructed to form HPPs total assembly relationship map. Information interaction with other digital organisms is obtained through knowledge-based interaction and interoperability interfaces (using Web service, data types are uniform attributes, nodes, and edges).
4.2. Twin data fusion and model interoperability 4.2.1. Data fusion in virtual-real space In the assembly-commissioning process of HPPs, multiple assemblycommissioning process is involved. These processes include related information, i.e. art size data, assembly-commissioning features, assembly-commissioning position, assembly-commissioning constraints (line surface fit, face line alignment). This information is then transmitted to the assembly-commissioning process by the laser scanner or sensor devices. In the virtual simulation, a large number of interactive data sets are generated, and the acquired information has multi-disciplinary and multi-disciplinary structured and unstructured data (i.e. surface defect pictures, rotation detection noise, 3D measurement point clouds, etc.), sequential and non-sequential data (i.e. multiple job line data overlay, multi-assembly coordinate data stacking, etc.). As shown in Fig. 6, the unstructured data is transformed into structured information using Artificial Intelligence models (i.e. CNN image processing model, RNN noise processing model, PCNNs point cloud model). In addition, for non-sequential data is transformed into sequential data through data alignment, data reorganization, and data sorting techniques. Through the knowledge fusion and knowledge processing, the scattered structural and sequential data obtained will be
4.2.2. Interoperability between digital twin models The information is exchanged between the assembly-commissioning digital twin models through knowledge-based interaction and interoperability interfaces. The information exchange process is shown in Fig. 7. In the sequential assembly-commissioning process, the performance digital twin model is used. The dimensional accuracy data set (i.e. tolerance analysis data, assembly-commissioning dimension chain generation data, assembly gap data, assembly accuracy prediction data, etc.) areused for dimensional accuracy assessment. Additionally, the performance accuracy data set (i.e. hydraulic response speed data, electromagnetic torque, etc.) are used for dimensional performance evaluation. When the assembly-commissioning dimensional accuracy and performance fail to meet the requirements, the assembly-commissioning control data sets (i.e. assembly-commissioning action requirement data, assembly-commissioning sequence requirement data) are readjusted for optimisation iteration until the completion of the assemblycommissioning of HPPs. 6
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Fig. 7. The assembly information exchange process of the digital twin models.
simulation data to form the twin data, the latest test and measurement data, the progress data, the performance data, the measured values of the assembly process state parameters, and the like are mapped to the digital twin model. And based on the established Integrate information models, theoretical values of key technology state parameters, and predictive analysis models to predict and analyze the assembly progress, accuracy, and performance of physical products in real-time.
4.3. Twin data-driven assembliability prediction and process optimization The HPPs assembly-commissioning operation require the assembly and commissioning processes are undertaken simultaneously in realtime. In addition, the assembly-commissioning quality factor constraints are high coupling. At the same time, for volatile assembly conditions and assembly objects, the general design process cannot meet the processing needs, and adaptive changes and commissionings should be made. On the basis of highly integrated twin data, data mining and modeling of twin data should be carried out, including measured data, performance data, simulation data, and deviation data (such as shape deviation, size deviation) in the assembly-commissioning process for analysis and deduction. Before the implementation of the assembly activities, according to the specific working conditions, the actual characteristic values of the parts, the corresponding process, predict the quality characteristics of the product, and avoid invalid assembly; In the assembly-commissioning process, the dynamic commissioning and real-time optimization of the assembly-commissioning process are realized for the corresponding working conditions and the actual characteristic values. High integration of the assembliability prediction and process optimization ensures the assembly quality of products. The assembly-commissioning process digital twin model as information carrier of product process, providing preliminary process information for later assembly activities. At the same time, the performance digital twin as a standard product process specification, can be used as a reference to compare with the actual product assembly process and quality information in physical workshop for deviation data. The process prediction and process optimization methods of the assembly-commissioning process are described as follows (see Fig. 8).
4.3.2. Assembly-commissioning process optimization method based on Pareto optimality By analyzing the typical assembly process defects of HPPs, artificial intelligence technology is introduced to solve key problems such as components grouping, tolerance analysis, assembly dimension chain generation, and assembly path generation. This step involves process knowledge modeling, knowledge refinement and knowledge optimization using data analysis. In addition, autonomous learning is used for independent process design, process optimization and process decision making. The multi-objective optimization problem is composed of multiple objective functions with related equations and inequality constraints. From a mathematical point of view, it can be described as follows:
min f1 x1, x2 , …, x n ……
min fr x1, x2 , ...,x n max fr + 1 x1, x2 , …, x n ……
max fm x1, x2 , ...,x n s. t . gi (x ) ≥ 0, i = 1, 2, ...,p
4.3.1. Twin data-driven assembliability prediction method Given the lack of analysis and foresight of the assembly process error accumulation in the assembly process and the impact of part manufacturing errors on the assembly process, the deep neural network prediction model is used to study the assembliability prediction of twin data based on virtual-real fusion (see Fig. 9). Based on the fusion of the physical measured data and the virtual
s. t . hj (x ) ≥ 0, j = 1, 2, ...,q Examine two decision vectors a, b∈X. a Pareto Dominate b, denoted a > b, if and only if:
{∀ i ∈ {1, 2, ...,n} fi (a) ≤ fi (b)} ∧ {∃ j ∈ {1, 2, ...,n} f j (a) < f j (b)} 7
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Fig. 8. Assembliability prediction and process optimization for the assembly-commissioning process.
nozzle-baffle valve and slide valve. Among the parameters of the assembly test, the rated parameters (rated current, rated flow and rated voltage) are mainly used to indicate the specifications of the servo valve. In addition, the performance is indicated by the performance parameters, i.e. hysteresis, resolution, linearity, symmetry, zero offset and temperature zero drift. The relationship between the performance parameters of the servo valve (output flow, input current, and load pressure) mainly includes load flow characteristics, no-load flow characteristics, pressure characteristics, and internal leakage characteristics.
If there is no decision vector Pareto dominates a decision vector in the whole parameter space, the decision vector is called Pareto optimal solution. Thus, all Pareto optimal solutions form the Pareto optimal solution set. Description of process optimization method based on Pareto optimal theory is under the condition of assembly-commissioning, the multi-performance parameters generated by the assembly process can form a weighted Pareto feasible solution space. Based on the Pareto optimal theory, assembly-commissioning process using massive data for continuous improvement and change response, and using measured data for process problem prediction, parameter dynamic commissioning, process iterative optimization, decision making, evaluation and evaluation. Assembly-commissioning process problem for multidisciplinary coupling, aiming at Pareto optimum, the self-adaptive optimization of assembly process is realized by constructing the mathematical model of qualitative mapping relationship between process parameters and assembly quality.
5.1. Construction of digital twin-driven assembly-commissioning test environment According to the requirements of the digital twin assembly, the digital twin-driven assembly-commissioning test environment was designed. As shown in Fig. 11, the experimental environment under construction includes a physical assembly test environment and a virtual assembly test environment. Fig. 12 shows the built-in digital twin assembly test bench. Based on the existing high-precision digital assembly test bench, multi-disciplinary commissioning test equipment is added to form a multi-professional real-time data measurement system for assembly-
5. A case study The electro-hydraulic servo valve is selected as the test prototype at the assembly site of the industrial partner. Fig. 10 shows a model of two-stage electro-hydraulic servo valve. The product adopts double nozzle-baffle force feedback structure, and it consists of moment motor,
Fig. 9. Twin data-driven assembliability prediction. 8
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Fig. 10. Model of a two-stage electro-hydraulic servo valve prototype.
5.2. Verification of the digital twin-driven assembly-commissioning
commissioning process, including: binocular vision camera, laser scanning equipment, electrical signal detection equipment, magnetic field detection equipment, hydraulic detection equipment. In the virtual test environment, the laser point cloud detection system, assembly size optics and pose detection system, machine-electric-liquid measurement system and magnetic field measurement system are mainly built. The system sensor is based on OPC UA for data acquisition. Based on the real-time processing of the MTConnect client data, the real-time data of the product assembly process is stored in the database, and the interface function is called to complete the data interaction between the assembly-commissioning process and the product performance in the virtual and physical spaces.
According to the digital twin-driven assembly-commissioning test environment, combined with the digital twin assembly theory, the digital twin assembly-commissioning system was developed, as shown in Fig. 13. It is used to verify the feasibility of the digital twin-driven assembly-commissioning. 5.2.1. Parts reverse modeling process The laser point cloud detection system is used to scan the physical space assembly state under a certain procedure. Furthermore, a point cloud scanning model is established in the virtual space, as shown in Fig. 14. First, the main valve body to be assembled is placed on the assembly work platform of the assembly test bench, and the high-
Fig. 11. Digital twin-driven assembly-commissioning test environment construction content. 9
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Fig. 12. Digital twin-driven assembly-commissioning test bench.
Fig. 13. Digital twin-driven assembly-commissioning system.
commissioning. Using the developed assembly-commissioning system, the point cloud data measured in Section 5.2.1 is registered with the theoretical model, and the registered digital model information is organically integrated with the part level and assembly level information described by the PMI in the theoretical model to establish a virtual reality fusion. The digital twin assembly model. As shown in Fig. 15, the green valve body model is the theoretical model constructed, and the pink valve body model is the processed point cloud scanning model. After the matching result analysis, the system calculates the method between the point cloud points and the theoretical model. To the difference. The value in the figure is the error information between a point in the valve
precision laser scanner detection module is turned on in the digital twin-driven assembly-commissioning system. Then, the transmission mechanism is used to adjust the scanning posture. Finally, a point cloud scan is performed and the model is visualized. The established point cloud scanning model is through multiple scans and organic processing, and the registration of each massive point cloud data maximizes the actual model features. 5.2.2. Twin data fusion and operation during assembly-commissioning The fusion of digital twin data refers to the fusion of 3D model data and the multi-disciplinary information, i.e. hydraulic, electrical signals and magnetic fields measured in real-time during assembly10
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The assembly-commissioning process is divided into three steps: selection and matching of parts, component assembly and assemblycommissioning. In the manual assembly method, the process prediction and optimization mainly depend on the empirical experience. Nevertheless, in the digital twin assembly-commissioning method, the assembly process data is collected through digital measuring equipment and various sensors, saved in the database and used to develop the artificial intelligence model for process prediction and optimization. Therefore, all kinds of control parameters are managed intelligently. The specific process steps and the control parameters are shown in Table 1. 5.2.4. Product performance testing and feedback The preliminary assembly of the second-stage electro-hydraulic servo valve is tested for performance evaluation. Relevant parameters are collected and presented in Fig. 17, including the no-load flow index parameter, load flow index parameter, pressure gain index parameter and internal leakage index parameter of the electro-hydraulic servo valve. The test results are analyzed and transmitted to the process optimization module to guide the assembly process optimization. The specific test methods include topics:
Fig. 14. Assembly point cloud model under a certain process.
body point cloud and the theoretical model. When the operation accesses the data of the fusion assembly relationship, the system will display the assembly feature information synchronously, and will prompt the next assembly operation.
Topic 1: Obtaining the no-load flow characteristic curve: the first step is to design a low-frequency control signal whose waveform is triangular and periodic. Then, the system test module is used to acquire the waveform diagram in a certain period. Finally, the obtained excitation current data and the flow data are processed, and the corresponding characteristic curve is output, as shown in Fig. 17(a). Topic 2: Acquisition of pressure characteristic curve: the first step is to disconnect two load ports and adjust the pressure value to the specified value. Then input the same type of signal as the no-load flow characteristic experiment, and use the system test module to obtain the waveform diagram in a certain period. The last step is to process the acquired excitation current data and the pressure data and generate the outputs, as shown in Fig. 17(b). Topic 3: Acquisition of load flow characteristic curve: the first step is to design multiple DC signals, input one of them into the system, and adjust the position of the throttle valve at the position of the oil port. Then, the image data in a certain period of time is acquired, the pressure data therein and the corresponding flow data are extracted, a file is formed, and a test is completed. The last step is to repeat the above process until the end of the test, and output the corresponding characteristic curve, as shown in Fig. 17(c). where: qR is the no-load servo valve flow, PS is the servo valve oil supply pressure, qL is the load servo valve flow, qC is the servo valve leakage flow, I is the servo valve current, PL is the servo valve load pressure, qp is the pilot leakage flow, q1 is the power level leakage flow. Topic:4 Acquisition of the internal leakage characteristic curve: the first step is to set the current in the spool to 0, and then design a lowfrequency control signal. The waveform of the signal is triangular
5.2.3. Assembly-commissioning operation process During the entire assembly-commissioning process, the assembly process and the commissioning process are continuously optimized and iterated. Among them, the assembly process is mainly applied to the dimensional requirements under the pose constraint, and the commissioning process is mainly applied to the dimensional requirements under the multidisciplinary performance constraints. For example, in the assembly process of the servo valve armature assembly and the valve body, it is necessary to continuously adjust the matching gap between the reaction rod and the valve core to adjust the zero opening. The steps are as follows: Step1: Initial assembly of the assembled armature assembly and the valve body to a pre-assembled position; Step2: Using a high-precision laser scanner to perform point cloud scanning, and register with the theoretical model to obtain assembly error, as shown in Fig. 16(a); Step3: Fine-tuning according to the actual assembly error, so that the assembly accuracy reaches the allowable range; Step4: Real-time data acquisition using hydraulic, electrical signals, electromagnetic test equipment, and data visualization, as shown in Fig. 16(b); Step5: The collected data is transmitted to the system analysis module for performance analysis, and the commissioning process optimization is guided, as shown in Fig. 16(c); Step6: Fine-tuning according to the process guidance document; Step7: Repeat Step4-Step6 until the performance requirements are met.
Fig. 15. virtual and real model fusion visualization. 11
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Fig. 16. Assembly-commissioning process data and process optimization visualization. Table 1 Digital twin assembly-commissioning process steps and control parameters. Assembly process steps Selection and matching of parts Component assembly
Control parameters Geometric dimensions of parts Assembly of valve sleeve assembly Assembly of throttle hole components Blockage of pressure nozzle seal Assembly of torque motor
Assembly-commissioning
Pre-pressure nozzle Preinstall valve sleeve, spool, throttle hole assembly, armature assembly, moment motor on valve body Fine assembly and commissioning: comprehensive commissioning of resolution, hysteresis, static characteristics, phase bandwidth, Bias, non-linearity, degree of asymmetry, zero position leakage, etc.
Return oil damper hole size Interference fit between throttle hole and oil filter Sealing plugging and interference fit of nozzle tail hole Armature clearance; Median moment; Pure steel degree; Hysteresis band Spacing between nozzle and baffle Geometric dimensions Geometric dimensions; The performance of the front stage is stable; Pre-stage pressure gain; Remaining magnetism of shell; Hydraulic zero position; Mechanical zero position; Electromagnetic zero position; etc.
5.4. Discussion
and periodic. The system test module is used to acquire the waveform diagram in a certain period. Finally, the obtained excitation current data and the flow data are processed, and the corresponding characteristic curve is generated, as shown in Fig. 17(d).
The comparison results are demonstrated in Table 2. In terms of the assembly efficiency, the assembly time was shortened to 37.5% of the conventional assembly method, and the assembly efficiency was improved significantly. In terms of assembly quality, the main performance indexes of the assembly method proposed in this paper were improved to different degrees compared with the traditional assembly methods. Among them, the zero leakage was reduced to 25% of the traditional assembly method, the overall assembly quality was improved, and the assembly is finished in only one attempt. Last but not the least, the qualification rate reached 90%, and the assembly quality was consistent.
5.3. Comparative analysis of assembly results Under the condition of parts selection, 10 sets of assembly tests were performed on the traditional artificial assembly method and the assembly method proposed in this paper. The average values of the performance indexes of 10 sets of data were used as representative indictors to be compared with traditional assembly technologies (see Table 2). 12
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Fig. 17. Performance characteristics.
6. Conclusion
commissioning is developed; (2) For the digital twin assembly-commissioning process, an assembly total factor information model is established, consisting of assembled part digital twin model, assembly process digital twin model and assembly performance digital twin model; (3) The information is exchanged between the assembled digital twin models through knowledge-based interaction and interoperability interfaces. Therefore, the multi-heterogeneous data generated from the assembly processes can be processed and aggregated; (4) For the large amount of data generated during the assembly process, an intelligent algorithm is used to establish an assembly predictability model. Using the Pareto optimal method, a mathematical model of the qualitative mapping relationship between process parameters and assembly quality is developed for an adaptive
With the rapid development of next-generation information technology such as artificial intelligence, industrial Internet, and industrial big data, the era of intelligent manufacturing has come. The digital twin technology has a great potential to be used in the field of product assembly. Based on the findings from reviews, this paper proposes a digital twin-driven assembly-commissioning method for high precision products with multidisciplinary coupling. The main contents are as follows: (1) Through the interaction and integration of data between the virtual space and the physical space of HPPs assembly workshops, a theoretical framework for the digital twin-driven assemblyTable 2 Comparison of main indicators of the two-stage servo valve. Parameters
Traditional assembly technology
Digital twin-driven assembly-commissioning
Primary qualification rate (%) Assembly commissioning time (h) Working pressure (oil port P, A,B) (MPa) Rated oil supply pressure (MPa) Rated flow (pressure difference △P = 7 MPa) (L/min) Amplitude bandwidth (−3 dB) (Hz) Phase bandwidth (−90˚) (Hz) Bias (%) Resolution (%) Degree of asymmetry (%) Non-linearity (%) Hysteresis (%) Temperature drift (−30–150°C) Zero position leakage (L/min)
50 About 8 2∼25 21 5, 10, 20 >100 >100 ≤±2 ≤1 ≤7.5 ≤7.5 ≤4 ≤±4 ≤0.8
90 About 3
≤ ± 1.5 ≤0.7 ≤7.0 ≤7.0 ≤2 ≤ ± 3.6 ≤0.2
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optimization of the assembly process.
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The proposed digital twin assembly method has been applied in the assembly process of a high-precision electro-hydraulic servo valve of aerospace type. It has been verified that this method can meet the assembly quality and efficiency requirements of HPPs. However, this research has limitations to be noted and further studied. Firstly, in case study validation, we only validated the military products whose assembly accuracy lower than micron level. Due to the limitation of equipment accuracy, for those products whose assembly accuracy is higher than micron level have not been validated. Additionally, this study mainly concentrates on the product assembly stage. There is no in-depth discussion on the correlation between the upstream and downstream of the assembly processes. Future work is needed for it to be applied throughout the whole life cycle of the products. Acknowledgement This work was supported in part by the National Natural Science Foundation of China (51475301), Shanghai Sailing Program (19YF1401600) and the Fundamental Research Funds for the Central Universities 2232019D3-32. Conflicts of interest None. References [1] Liu Jianhua, Sun Qingchao, Cheng Hui, et al., The State-of-the-art, connotation and developing trends of the products assembly technology, J. Mech. Eng. 54 (11) (2018) 2–28. [2] S Jayaram, U Jayaram, Y Wang, et al., VADE: a virtual assembly design environment, Comput. Graph. Appl. IEEE 19 (6) (1999) 44–50. [3] G Kaufman S, H Wilson R, R Jones, et al., The archimedes 2 mechanical assembly planning system, IEEE International Conference on Robotics & Automation, 1996. [4] Peng Xiang, Liu Zhenyu, Tan Jianrong, et al., Design model simplification for complex products based on changeable correlation analysis, J. Comput.-Aided Des. Comput. Graph. 25 (8) (2013) 1245–1254. [5] Zhou Sihang, Liu Zhenyu, Tan Jianrong, Deviation propagation model of assembly sequence and quality evaluation approach based on degree of dimensional variation, J. Mech. Eng. 47 (2) (2011) 1–8. [6] Zhang Zhixian, Liu Jianhua, Ning Ruxin, Physical assembly process simulation based on Multi-rigid-body dynamics in virtual assembly, J. Mech. Eng. 49 (5) (2013) 90–99. [7] Liu mi, Liu Jianhua, He Yongxi, et al., Research on assembly path planning and optimization of complex structures, J. Mech. Eng. 49 (9) (2013) 97–105. [8] Liu Jiashun, Liu Jianhua, Wang Zhibin, et al., Integrated information model of complex cable harness in virtual environment, Comput. Integr. Manuf. Syst. 19 (5) (2013) 964–971. [9] Hu Wei, Liu Jianhua, Jiang Ke, et al., Assembly precision prediction method for spacecraft based on 3D model, Comput. Integr. Manuf. Syst. 19 (5) (2013) 990–999. [10] Chen Zhehan, Du Fuzhou, Tang Xiaoging, Key measurement characteristics based inspection data modeling for aircraft assembly, Acta Aeronautica et Astronautica Sinica 33 (11) (2012) 2143–2152. [11] Yang Fan, Tang Xiaoqing, Duan Guijiang, Searching model of change propagation paths for mechanical product based on characteristic linkage network, J. Mech. Eng. 47 (19) (2011) 97–106. [12] Du Fuzhou, Chen Zhehan, Tang Xiaoqing, Precision analysis of iGPS measurement field and its application, Acta Aeronautica et Astronautica Sinica 33 (9) (2012) 1737–1745. [13] Zhen Xijin, Wu Dianlian, Fan Xiumin, et al., Distributed parallel collaborative virtual assembly system for complex products, Comput. Integr. Manuf. Syst. 14 (10) (2008) 1990–1995. [14] Qiu Shiguang, Fan Xiumin, Wu Dianliang, et al., Virtual human modeling for interactive assembly and disassembly operation in virtual reality environment, Int. J.
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Full length Article
A digital twin-driven approach for the assembly-commissioning of high precision products
T
⁎
Sun Xuemina, Bao Jinsonga, , Li Jiea, Zhang Yimingb, Liu Shimina, Zhou Bina a b
College of Mechanical Engineering, Donghua University, Shanghai 201620, China College of Literature, Science and the Arts, The University of Michigan, Ann Arbor, MI 48109, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Digital twin Multidisciplinary coupling High precision Assembly-commissioning Data fusion Interoperability
High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.
1. Introduction As one of the critical elements, HPPs are mainly used in aerospace, marine, chemical and other fields. The quality of the machinery and assembly is one of the key factors determining the quality of the final product. With the continuous improvement of the machining precision of high precision CNC machine tools, the machining accuracy of product parts has reached a relatively high level. HPPs have complicated assembly schedules and high precision requirements, and need to reach micron or even sub-micron scale in the process of assembly. The process is further complicated by the interactions between electromagnetic, physical, hydraulic, structural characteristics and the multidisciplinary problems in the domains of machinery, electronics and hydraulics. Furthermore, the error in the assembly quality consistency is required to be less than 1%. Therefore, research on the assembly process of HPPs has become an effective way to improve production quality. For a long time, the assembly process for HPPs have always been based on manual experience. Only high skill operators are capable of these challenging assembly operations. However, it is still difficult to guarantee the consistency and accuracy of the assembly, i.e. the onetime pass rate of the product is relatively low. In addition, the rework and re-assembly of unqualified products increased the labor intensity of workers, which affects the efficiency of the assembly to a certain extent. With the increasing application of HPPs and the growing demand for ⁎
technology, how to improve the quality and efficiency of assembly has become an urgent problem. Therefore, this paper proposes a digital twin-driven assembly-commissioning approach for HPPs, aiming at improving both assembly quality and efficiency. The rest of this paper is organized as follows. Section 2 provides a brief review of existing digital assembly technologies. Section 3 provides a theoretical framework for digital twin-driven assembly-commissioning and the introduction of the virtual and physical assembly space. Section 4 introduces the operation method of digital twin-driven assembly-commissioning, including the method of modeling the total factor information model of digital twin, the fusion of twin data, the interoperability method between models, the data pre-parable predictability of twin data, and the assembly-commissioning process optimization. In Section 5, a case study of the assembly process of a highprecision electro-hydraulic servo valve is used to verify the feasibility of the method. The last section is the conclusion of this research. 2. Literature review The rapid development of high-tech technologies (i.e. artificial intelligence, computer simulation, industrial big data) have promoted the digitalisation of assembly. The development process of digital assembly can be divided into three stages as shown in Fig. 1. The first phase (around Year 2000), the virtual assembly technology
Corresponding author. E-mail address: [email protected] (J. Bao).
https://doi.org/10.1016/j.rcim.2019.101839 Received 29 March 2019; Received in revised form 8 July 2019; Accepted 11 July 2019 0736-5845/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Digital assembly development stage.
emphasizing on large flexible docking assembly through real-time measurement. Ke [18–22] studied key issues such as position accuracy compensation and position control of aircraft assembly docking. Shao [23] presented the virtual assembly architecture and assembly process simulation based on physical properties. Liu and Zhang [24,25] proposed a virtual assembly technique based on precision and physical properties to simulate various problems in the assembly site in real time from the perspective of process, precision and physical properties. The quasi-physical virtual assembly is based on the physical properties of the product. It is found that quasi-physical virtual assembly is mainly applied in the assembly of aircraft for the interaction of dimensional accuracy data between physical and virtual spaces. The quasi-physical virtual assembly achieves the initial integration of virtual and physical data, so that the assembly accuracy of the product can meet the requirements. However, this technology is not capable of databased product performance prediction and process dynamic optimization. Therefore, it cannot be used in the assembly of HPPs which involves the multidisciplinary coupling issue.
development period, focusing on process design and verification. The second phase (around Year 2010), the development phase of the quasi-physical virtual assembly technology, focusing on the assembly site. The third stage (around Year 2020), the development period of digital twin assembly technology, focusing on integration of assembly and commissioning. 2.1. Virtual assembly technology Virtual assembly refers to the use of virtual reality technology, computer graphics, artificial intelligence technology and simulation technology to construct virtual environment and product virtual model, for the purpose of interactive analysis and simulation of assembly [1]. The Washington State University and the National Institute of Standards and Technology (NIST) [2] jointly developed the VADE system, which used virtual reality technology to import CAD models into the system for virtual assembly operations. The Sandia National Laboratory [3] developed a set of Archimedes virtual assembly systems for the production of interactive assembly planning that optimize and inspect assembly process. Tan [4,5] studied the hybrid modeling, semantic-oriented expression and assembly sequence deviation transfer model of virtual products. Liu and Ning [6–9] presented the assembly properties of flexible products, flexible cables, accumulation of assembly deviations and accuracy prediction. Tang [10–12] studied flexible assembly for key assembly characteristics in aircraft component assembly. Wu and Fan [13,14] proposed the collaborative product assembly operation simulation of complex products in virtual environments, assembly modeling for operational process planning and virtual human modeling for interactive assembly and disassembly operations. At present, the virtual assembly technology has been applied, to improve the assembly quality and efficiency of the product. However, the majority of the research focuses on the assembly of large-scale products. There are few studies on the assembly of HPPs. Because the virtual assembly focuses on the digital simulation in the virtual environment, it cannot deal with the external interference factors in the physical assembly process. It is impossible to achieve assembly accuracy requirements for HPPs.
2.3. Digital twin assembly The digital twin assembly is in the second stage of the virtualphysical fusion assembly. Based on the virtual assembly information model and the quantitative calculation of assembly quality, the virtualphysical interaction, data fusion, decision analysis and iterative optimization in the whole assembly process are realized using twin data of assembly context. The concept of digital twin was first proposed by Professor Grieves [26,27]in the product lifecycle management class. Ferguson [28] used Siemens STAR CCM + and digital twin to develop an accurate virtual model, and it is also used in product development for accurately reproducing physical and performance characteristics of products. Tao [29–31] established the digital twin workshop (DTW) model, expounding the system composition, operation mechanism and key technologies of the digital twin workshop to guide the construction of a new generation of digital workshops. In order to solve the problem of lack of interaction between physical space and virtual space in manufacturing, Bao [32] proposed the method integrates product, process, operation digital twin as well as the interoperability mode between the digital twin. Oyekan [33] uses of a virtual reality digital twin of a physical layout as a mechanism to understand human reactions to both predictable and unpredictable robot motions. Lu [34] illustrates a generic architecture for cloud-based manufacturing equipment based on technologies such as digital twin and big data analytics. Petković [35] proposes a ToM based human intention estimation algorithm for flexible robotized warehouses, in order to demonstrate the scalability of the approach to larger ware-houses. They propose to use virtual reality digital ware-house twins in order to simulate worker behavior in
2.2. Quasi-physical virtual assembly Quasi-physical virtual assembly [15] is the initial stage of the virtual-real fusion assembly. This technology is mainly used for the development of assembly process, assembly information model, model evolution law and visualization method. For example, the quasi-physical assembly has been widely used in the field of flexible docking of large components such as airplanes. Jafar and Kayani [16,17] took the lead in putting forward the concept of Measurement Assisted Assembly, 2
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commissioning is established. As shown in Fig. 2, the framework structure includes physical assembly space, virtual assembly space and digital twin model. The main content of the research is the operation method of digital twin assembly, it includes: A. Construction method of assembly total factor information model based on the digital twin. B. Twin data fusion method for the assembly process, and interoperability method between digital twin models. C. Twin data-driven assembliability prediction method, and assembly process dynamic optimization method.
reality. In order to achieve the real-time data feedback of digital twin, the fibre channel (FC) switch based on field programmable gate array (FPGA) is designed and implemented due to its high speed, low latency, and high- performance transmission capacities by Tao [36]. Nikolakis [37] focuses on a Cyber-Physical System (CPS) for enabling humanrobot collaboration based on real-time evaluation of safety distance and a closed-loop control for triggering collision preventive actions. As one of the core technologies in CPS, there is a great potential to apply digital twin into human-robot collaboration studies. In the field of manufacturing, the application of digital twin is mainly in the construction of digital workshops, and the majority of the research is based on theoretical research such as the model framework of digital twin without physical application in industries. Some of the literature has carried out preliminary explorations on digital twin assembly technology. Liu [38] proposed a digital twin assembly workshop architecture covering the physical assembly workshop, virtual assembly workshop, workshop twin data and assembly workshop service system in the field of aerospace assembly, and studied the real-time perception of physical assembly workshop data. There are three key technologies such as acquisition, virtual assembly shop modeling and simulation operation technology, and assembly workshop production control. Liu [39-41] proposed a complex product assembly process driven by digital twin. Taking satellite product assembly as the object, using augmented reality technology to assist assembly, the assembly process design method based on a 3D model, a 3D assembly process demonstration model and lightweight display technology were studied. In conclusion, the digital twin assembly inherits the advantages of virtual assembly and quasi-physical virtual assembly, and combines the rapid development of advanced technologies such as computing technology, artificial intelligence and data measurement to realize the virtual space and physics of the assembly process. The digital twin assembly technology meets the dimensional accuracy requirements and dimensional performance requirements in the assembly process of HPPs, and provides a new method for quickly breaking through the assembly problems of HPPs.
3.1. Physical assembly space The physical entities in the physical assembly space mainly include: people, equipment, components and products. Process generated from the simulation in the virtual space can be transferred to the physical assembly space for the practical production. The physical assembly space under digital twin has the following advantages compared with traditional physical assembly space: (1) A series of sensors and digital measurement tools have been used for real-time data acquisition and dynamic tracking in the assembly process; (2) The utilisation of digital and intelligent equipment helps the improvement of production efficiency, quality, as well as the collection and storage of real-time data. (3) The quality control of the assembly process adopts intelligent management; (4) An intelligent algorithm is developed and used for dynamic optimisation of the assembly processes. (5) Interactive fusion between multiple heterogeneous elements of the shop floor is achieved. 3.2. Virtual assembly space The digital twin model of the virtual assembly space is a digital mapping of the physical space entity model, integrating and blending features such as geometry, physics, behavior, and rules. Three digital twin models are built in the virtual assembly shop according to the assembly process: assembly component digital twin model, process digital twin model and assembly performance digital twin model. In the horizontal directions, the three digital twin models establish data interaction and fusion between the physical entity of the component and the 3D geometric model of the component, data interaction and fusion between the physical assembly process and the virtual assembly
3. The theoretical framework for digital twin-driven assemblycommissioning The research aims to apply the digital twin technology in the assembly process of HPPs. Based on the virtual-physical mapping relationship between physical assembly space and virtual assembly space, the theoretical framework of digital twin-driven assembly-
Fig. 2. The framework of digital twin-driven assembly-commissioning. 3
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Fig. 3. Virtual-physical dimension deviation calculation.
Step3: Through special high-precision indirect measuring equipment, the internal features of the cavity are obtained; Step4: For the measured indirect data, the closed contour data information is generated by the mathematical model analysis, and finally integrated into the point cloud data.
simulation, and between the physical product and the product performance model. In the vertical direction, the data interaction and interoperability between the assembly component digital twin model, the process digital twin model and the assembly performance digital twin model are performed. In the actual assembly process, the collected realtime assembly data and analysis results are used to realize the dynamic optimization commissioning of the assembly process based on the intelligent algorithm.
(3) Fusion of the theoretical model and the physical model The key assembly-commissioning features are used as the registration points to realize the fusion of the theoretical model and the physical model, and the digital twin model is constructed. The assemblycommissioning data fusion and deviation calculation of the manufacturing deviation are shown in Fig. 3.
4. The operation method for digital twin-driven assemblycommissioning 4.1. Assembly total factor information model based on digital twin Digital twin refers to a comprehensive physical and functional description of component, product, or system. Development of the digital twin model of assembly-commissioning part, assembly-commissioning process and assembly-commissioning performance is essential for subsequent implementation of the digital twin assembly-commissioning operation process.
4.1.2. Assembly-commissioning process digital twin model The digital twin model for the assembly-commissioning process mainly includes dynamic iterative optimization of the process-parameters and real-time iterative optimization of the assembly-commissioning processing. The specific iterative process is shown in Fig. 4. (1) Process-parameters optimization
4.1.1. Assembly-commissioning part digital twin model The assembly-commissioning part digital twin model is a fusion of theoretical and physical models.
The process refers to planning and determining the assembly route and process-parameters from the part to the assembly under the control of a certain target and the actual assembly resources. The assembly process of the traditional virtual assembly is to use Computer Aided Process Planning to receive product design information from the CAD system, and rely on the process knowledge base to assist the process designer to make decisions and generate assembly process-parameters. Although the method improves the process generation efficiency to a certain extent, the generated assembly process is static and cannot cope with the influence of disturbance factors occurring in the physical assembly process-parameters, and thus cannot make real-time dynamic response according to the processing deviation, which affects the final product quality. Based on the traditional process generation, combined with real-time data acquisition technology and intelligent algorithms, real-time dynamic commissioning and optimization of process-parameters can be realized. Its process-parameters information includes product information, process information, and resource information.
(1) Theoretical model The part theory model can be built using the Model Based Definition (MBD) based modeling techniques. The established part MBD model involves geometric information and non-geometric information. Nongeometric information mainly includes material properties, 3D annotation information, and engineering annotations, where engineering annotations primarily describe the product definition information that must be provided by the process plan. (2) Physical model The physical model can be built by point cloud scanning. The research on measured data is relatively mature, especially the assemblycommissioning feature recognition, modeling and registration of sparse large amounts of data. For an assembly-commissioning part, the traditional point cloud scanning method is to scan the surface of the workpiece to be tested without any difference, such as assembly-commissioning features, reference features and other key parts, resulting in huge data volume and data redundancy. The reverse modeling technology of parts considering key assembly-commissioning features, realtime acquisition of point cloud data of assembly-commissioning parts, the specific steps are as follows:
(2) Optimization of assembly-commissioning processing The real-time iterative optimization of the assembly-commissioning processing reflects the data interaction process between the physical space and the virtual space. The physical space assembly-commissioning processing is assembled by receiving process-parameters optimized from the assembly-commissioning process. Real-time acquisition of assembly-commissioning process data by sensors and other devices, uploading the collected data to the virtual space, the virtual assemblycommissioning processing updates the state according to the real-time state of the physical workshop, and compares the physical assemblycommissioning data of the physical space with the predefined theoretical data. Determine whether the comparison error exceeds the specified threshold, optimize with an intelligent algorithm, and form an optimized assembly-commissioning processing file to perform virtual process-parameters simulation.
Step1: Determining assembly-commissioning part assembly-commissioning features, datum features, boundary features, etc.; Step2: According to the geometric shape of the parts, the key assembly-commissioning features, determine the motion trajectory of the part laser scanning system, the rotation of the turntable, the scanning frequency of the special measuring equipment, and obtain the high-precision measurement of the 3D feature top point cloud; 4
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Fig. 4. Iterative optimization of process-parameters and assembly-commissioning processing.
finally determine the main parameters affecting the performance indexes of high-precision complex products. In addition, although each dynamic analysis model reflects the performance of different aspects of high-precision assembly components, they affect each other and restrict each other. In this paper, the statistical analysis of performance indicators is designed and utilised to study the correlation method, and the linear fitting degree of each index is analyzed. In high-precision assembly assemblies, small differences in geometry parameters and material properties can be amplified in assembly performance, resulting in product failure. Therefore, it is necessary to consider the geometric deviation and its transfer relationship in the original static characteristic equation, and then deduct the theoretical formula and enrich the theoretical model to make it closer to the physical model.
4.1.3. Assembly performance digital twin model The established assembly-commissioning performance digital twin model is mainly used for assembly-commissioning quality assessment, including appearance quality, assembly error, dimensional accuracy, and overall performance. Taking a high-precision electro-hydraulic servo valve as an example, it is necessary to evaluate not only the dimensional accuracy under the pose constraint but also the multi-disciplinary performance requirements regarding dimensional parameters after assembly. As shown in Fig. 5, the correlation analysis method is used to determine the gray relational order of the geometrical, physical, electromagnetic, and hydraulic factors of high-precision assembly parts for each performance index, comprehensively analyze the different gray correlation sequences obtained by different data normalization methods, and associate multiple gray correlations. The geometric, physical, electromagnetic and hydraulic parameters ranked in the front are the main geometric parameters affecting the performance index, to
Fig. 5. Performance digital twin models based on correlation analysis. 5
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Fig. 6. Twin data processing and fusion.
constructed to form HPPs total assembly relationship map. Information interaction with other digital organisms is obtained through knowledge-based interaction and interoperability interfaces (using Web service, data types are uniform attributes, nodes, and edges).
4.2. Twin data fusion and model interoperability 4.2.1. Data fusion in virtual-real space In the assembly-commissioning process of HPPs, multiple assemblycommissioning process is involved. These processes include related information, i.e. art size data, assembly-commissioning features, assembly-commissioning position, assembly-commissioning constraints (line surface fit, face line alignment). This information is then transmitted to the assembly-commissioning process by the laser scanner or sensor devices. In the virtual simulation, a large number of interactive data sets are generated, and the acquired information has multi-disciplinary and multi-disciplinary structured and unstructured data (i.e. surface defect pictures, rotation detection noise, 3D measurement point clouds, etc.), sequential and non-sequential data (i.e. multiple job line data overlay, multi-assembly coordinate data stacking, etc.). As shown in Fig. 6, the unstructured data is transformed into structured information using Artificial Intelligence models (i.e. CNN image processing model, RNN noise processing model, PCNNs point cloud model). In addition, for non-sequential data is transformed into sequential data through data alignment, data reorganization, and data sorting techniques. Through the knowledge fusion and knowledge processing, the scattered structural and sequential data obtained will be
4.2.2. Interoperability between digital twin models The information is exchanged between the assembly-commissioning digital twin models through knowledge-based interaction and interoperability interfaces. The information exchange process is shown in Fig. 7. In the sequential assembly-commissioning process, the performance digital twin model is used. The dimensional accuracy data set (i.e. tolerance analysis data, assembly-commissioning dimension chain generation data, assembly gap data, assembly accuracy prediction data, etc.) areused for dimensional accuracy assessment. Additionally, the performance accuracy data set (i.e. hydraulic response speed data, electromagnetic torque, etc.) are used for dimensional performance evaluation. When the assembly-commissioning dimensional accuracy and performance fail to meet the requirements, the assembly-commissioning control data sets (i.e. assembly-commissioning action requirement data, assembly-commissioning sequence requirement data) are readjusted for optimisation iteration until the completion of the assemblycommissioning of HPPs. 6
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Fig. 7. The assembly information exchange process of the digital twin models.
simulation data to form the twin data, the latest test and measurement data, the progress data, the performance data, the measured values of the assembly process state parameters, and the like are mapped to the digital twin model. And based on the established Integrate information models, theoretical values of key technology state parameters, and predictive analysis models to predict and analyze the assembly progress, accuracy, and performance of physical products in real-time.
4.3. Twin data-driven assembliability prediction and process optimization The HPPs assembly-commissioning operation require the assembly and commissioning processes are undertaken simultaneously in realtime. In addition, the assembly-commissioning quality factor constraints are high coupling. At the same time, for volatile assembly conditions and assembly objects, the general design process cannot meet the processing needs, and adaptive changes and commissionings should be made. On the basis of highly integrated twin data, data mining and modeling of twin data should be carried out, including measured data, performance data, simulation data, and deviation data (such as shape deviation, size deviation) in the assembly-commissioning process for analysis and deduction. Before the implementation of the assembly activities, according to the specific working conditions, the actual characteristic values of the parts, the corresponding process, predict the quality characteristics of the product, and avoid invalid assembly; In the assembly-commissioning process, the dynamic commissioning and real-time optimization of the assembly-commissioning process are realized for the corresponding working conditions and the actual characteristic values. High integration of the assembliability prediction and process optimization ensures the assembly quality of products. The assembly-commissioning process digital twin model as information carrier of product process, providing preliminary process information for later assembly activities. At the same time, the performance digital twin as a standard product process specification, can be used as a reference to compare with the actual product assembly process and quality information in physical workshop for deviation data. The process prediction and process optimization methods of the assembly-commissioning process are described as follows (see Fig. 8).
4.3.2. Assembly-commissioning process optimization method based on Pareto optimality By analyzing the typical assembly process defects of HPPs, artificial intelligence technology is introduced to solve key problems such as components grouping, tolerance analysis, assembly dimension chain generation, and assembly path generation. This step involves process knowledge modeling, knowledge refinement and knowledge optimization using data analysis. In addition, autonomous learning is used for independent process design, process optimization and process decision making. The multi-objective optimization problem is composed of multiple objective functions with related equations and inequality constraints. From a mathematical point of view, it can be described as follows:
min f1 x1, x2 , …, x n ……
min fr x1, x2 , ...,x n max fr + 1 x1, x2 , …, x n ……
max fm x1, x2 , ...,x n s. t . gi (x ) ≥ 0, i = 1, 2, ...,p
4.3.1. Twin data-driven assembliability prediction method Given the lack of analysis and foresight of the assembly process error accumulation in the assembly process and the impact of part manufacturing errors on the assembly process, the deep neural network prediction model is used to study the assembliability prediction of twin data based on virtual-real fusion (see Fig. 9). Based on the fusion of the physical measured data and the virtual
s. t . hj (x ) ≥ 0, j = 1, 2, ...,q Examine two decision vectors a, b∈X. a Pareto Dominate b, denoted a > b, if and only if:
{∀ i ∈ {1, 2, ...,n} fi (a) ≤ fi (b)} ∧ {∃ j ∈ {1, 2, ...,n} f j (a) < f j (b)} 7
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Fig. 8. Assembliability prediction and process optimization for the assembly-commissioning process.
nozzle-baffle valve and slide valve. Among the parameters of the assembly test, the rated parameters (rated current, rated flow and rated voltage) are mainly used to indicate the specifications of the servo valve. In addition, the performance is indicated by the performance parameters, i.e. hysteresis, resolution, linearity, symmetry, zero offset and temperature zero drift. The relationship between the performance parameters of the servo valve (output flow, input current, and load pressure) mainly includes load flow characteristics, no-load flow characteristics, pressure characteristics, and internal leakage characteristics.
If there is no decision vector Pareto dominates a decision vector in the whole parameter space, the decision vector is called Pareto optimal solution. Thus, all Pareto optimal solutions form the Pareto optimal solution set. Description of process optimization method based on Pareto optimal theory is under the condition of assembly-commissioning, the multi-performance parameters generated by the assembly process can form a weighted Pareto feasible solution space. Based on the Pareto optimal theory, assembly-commissioning process using massive data for continuous improvement and change response, and using measured data for process problem prediction, parameter dynamic commissioning, process iterative optimization, decision making, evaluation and evaluation. Assembly-commissioning process problem for multidisciplinary coupling, aiming at Pareto optimum, the self-adaptive optimization of assembly process is realized by constructing the mathematical model of qualitative mapping relationship between process parameters and assembly quality.
5.1. Construction of digital twin-driven assembly-commissioning test environment According to the requirements of the digital twin assembly, the digital twin-driven assembly-commissioning test environment was designed. As shown in Fig. 11, the experimental environment under construction includes a physical assembly test environment and a virtual assembly test environment. Fig. 12 shows the built-in digital twin assembly test bench. Based on the existing high-precision digital assembly test bench, multi-disciplinary commissioning test equipment is added to form a multi-professional real-time data measurement system for assembly-
5. A case study The electro-hydraulic servo valve is selected as the test prototype at the assembly site of the industrial partner. Fig. 10 shows a model of two-stage electro-hydraulic servo valve. The product adopts double nozzle-baffle force feedback structure, and it consists of moment motor,
Fig. 9. Twin data-driven assembliability prediction. 8
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Fig. 10. Model of a two-stage electro-hydraulic servo valve prototype.
5.2. Verification of the digital twin-driven assembly-commissioning
commissioning process, including: binocular vision camera, laser scanning equipment, electrical signal detection equipment, magnetic field detection equipment, hydraulic detection equipment. In the virtual test environment, the laser point cloud detection system, assembly size optics and pose detection system, machine-electric-liquid measurement system and magnetic field measurement system are mainly built. The system sensor is based on OPC UA for data acquisition. Based on the real-time processing of the MTConnect client data, the real-time data of the product assembly process is stored in the database, and the interface function is called to complete the data interaction between the assembly-commissioning process and the product performance in the virtual and physical spaces.
According to the digital twin-driven assembly-commissioning test environment, combined with the digital twin assembly theory, the digital twin assembly-commissioning system was developed, as shown in Fig. 13. It is used to verify the feasibility of the digital twin-driven assembly-commissioning. 5.2.1. Parts reverse modeling process The laser point cloud detection system is used to scan the physical space assembly state under a certain procedure. Furthermore, a point cloud scanning model is established in the virtual space, as shown in Fig. 14. First, the main valve body to be assembled is placed on the assembly work platform of the assembly test bench, and the high-
Fig. 11. Digital twin-driven assembly-commissioning test environment construction content. 9
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Fig. 12. Digital twin-driven assembly-commissioning test bench.
Fig. 13. Digital twin-driven assembly-commissioning system.
commissioning. Using the developed assembly-commissioning system, the point cloud data measured in Section 5.2.1 is registered with the theoretical model, and the registered digital model information is organically integrated with the part level and assembly level information described by the PMI in the theoretical model to establish a virtual reality fusion. The digital twin assembly model. As shown in Fig. 15, the green valve body model is the theoretical model constructed, and the pink valve body model is the processed point cloud scanning model. After the matching result analysis, the system calculates the method between the point cloud points and the theoretical model. To the difference. The value in the figure is the error information between a point in the valve
precision laser scanner detection module is turned on in the digital twin-driven assembly-commissioning system. Then, the transmission mechanism is used to adjust the scanning posture. Finally, a point cloud scan is performed and the model is visualized. The established point cloud scanning model is through multiple scans and organic processing, and the registration of each massive point cloud data maximizes the actual model features. 5.2.2. Twin data fusion and operation during assembly-commissioning The fusion of digital twin data refers to the fusion of 3D model data and the multi-disciplinary information, i.e. hydraulic, electrical signals and magnetic fields measured in real-time during assembly10
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The assembly-commissioning process is divided into three steps: selection and matching of parts, component assembly and assemblycommissioning. In the manual assembly method, the process prediction and optimization mainly depend on the empirical experience. Nevertheless, in the digital twin assembly-commissioning method, the assembly process data is collected through digital measuring equipment and various sensors, saved in the database and used to develop the artificial intelligence model for process prediction and optimization. Therefore, all kinds of control parameters are managed intelligently. The specific process steps and the control parameters are shown in Table 1. 5.2.4. Product performance testing and feedback The preliminary assembly of the second-stage electro-hydraulic servo valve is tested for performance evaluation. Relevant parameters are collected and presented in Fig. 17, including the no-load flow index parameter, load flow index parameter, pressure gain index parameter and internal leakage index parameter of the electro-hydraulic servo valve. The test results are analyzed and transmitted to the process optimization module to guide the assembly process optimization. The specific test methods include topics:
Fig. 14. Assembly point cloud model under a certain process.
body point cloud and the theoretical model. When the operation accesses the data of the fusion assembly relationship, the system will display the assembly feature information synchronously, and will prompt the next assembly operation.
Topic 1: Obtaining the no-load flow characteristic curve: the first step is to design a low-frequency control signal whose waveform is triangular and periodic. Then, the system test module is used to acquire the waveform diagram in a certain period. Finally, the obtained excitation current data and the flow data are processed, and the corresponding characteristic curve is output, as shown in Fig. 17(a). Topic 2: Acquisition of pressure characteristic curve: the first step is to disconnect two load ports and adjust the pressure value to the specified value. Then input the same type of signal as the no-load flow characteristic experiment, and use the system test module to obtain the waveform diagram in a certain period. The last step is to process the acquired excitation current data and the pressure data and generate the outputs, as shown in Fig. 17(b). Topic 3: Acquisition of load flow characteristic curve: the first step is to design multiple DC signals, input one of them into the system, and adjust the position of the throttle valve at the position of the oil port. Then, the image data in a certain period of time is acquired, the pressure data therein and the corresponding flow data are extracted, a file is formed, and a test is completed. The last step is to repeat the above process until the end of the test, and output the corresponding characteristic curve, as shown in Fig. 17(c). where: qR is the no-load servo valve flow, PS is the servo valve oil supply pressure, qL is the load servo valve flow, qC is the servo valve leakage flow, I is the servo valve current, PL is the servo valve load pressure, qp is the pilot leakage flow, q1 is the power level leakage flow. Topic:4 Acquisition of the internal leakage characteristic curve: the first step is to set the current in the spool to 0, and then design a lowfrequency control signal. The waveform of the signal is triangular
5.2.3. Assembly-commissioning operation process During the entire assembly-commissioning process, the assembly process and the commissioning process are continuously optimized and iterated. Among them, the assembly process is mainly applied to the dimensional requirements under the pose constraint, and the commissioning process is mainly applied to the dimensional requirements under the multidisciplinary performance constraints. For example, in the assembly process of the servo valve armature assembly and the valve body, it is necessary to continuously adjust the matching gap between the reaction rod and the valve core to adjust the zero opening. The steps are as follows: Step1: Initial assembly of the assembled armature assembly and the valve body to a pre-assembled position; Step2: Using a high-precision laser scanner to perform point cloud scanning, and register with the theoretical model to obtain assembly error, as shown in Fig. 16(a); Step3: Fine-tuning according to the actual assembly error, so that the assembly accuracy reaches the allowable range; Step4: Real-time data acquisition using hydraulic, electrical signals, electromagnetic test equipment, and data visualization, as shown in Fig. 16(b); Step5: The collected data is transmitted to the system analysis module for performance analysis, and the commissioning process optimization is guided, as shown in Fig. 16(c); Step6: Fine-tuning according to the process guidance document; Step7: Repeat Step4-Step6 until the performance requirements are met.
Fig. 15. virtual and real model fusion visualization. 11
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Fig. 16. Assembly-commissioning process data and process optimization visualization. Table 1 Digital twin assembly-commissioning process steps and control parameters. Assembly process steps Selection and matching of parts Component assembly
Control parameters Geometric dimensions of parts Assembly of valve sleeve assembly Assembly of throttle hole components Blockage of pressure nozzle seal Assembly of torque motor
Assembly-commissioning
Pre-pressure nozzle Preinstall valve sleeve, spool, throttle hole assembly, armature assembly, moment motor on valve body Fine assembly and commissioning: comprehensive commissioning of resolution, hysteresis, static characteristics, phase bandwidth, Bias, non-linearity, degree of asymmetry, zero position leakage, etc.
Return oil damper hole size Interference fit between throttle hole and oil filter Sealing plugging and interference fit of nozzle tail hole Armature clearance; Median moment; Pure steel degree; Hysteresis band Spacing between nozzle and baffle Geometric dimensions Geometric dimensions; The performance of the front stage is stable; Pre-stage pressure gain; Remaining magnetism of shell; Hydraulic zero position; Mechanical zero position; Electromagnetic zero position; etc.
5.4. Discussion
and periodic. The system test module is used to acquire the waveform diagram in a certain period. Finally, the obtained excitation current data and the flow data are processed, and the corresponding characteristic curve is generated, as shown in Fig. 17(d).
The comparison results are demonstrated in Table 2. In terms of the assembly efficiency, the assembly time was shortened to 37.5% of the conventional assembly method, and the assembly efficiency was improved significantly. In terms of assembly quality, the main performance indexes of the assembly method proposed in this paper were improved to different degrees compared with the traditional assembly methods. Among them, the zero leakage was reduced to 25% of the traditional assembly method, the overall assembly quality was improved, and the assembly is finished in only one attempt. Last but not the least, the qualification rate reached 90%, and the assembly quality was consistent.
5.3. Comparative analysis of assembly results Under the condition of parts selection, 10 sets of assembly tests were performed on the traditional artificial assembly method and the assembly method proposed in this paper. The average values of the performance indexes of 10 sets of data were used as representative indictors to be compared with traditional assembly technologies (see Table 2). 12
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Fig. 17. Performance characteristics.
6. Conclusion
commissioning is developed; (2) For the digital twin assembly-commissioning process, an assembly total factor information model is established, consisting of assembled part digital twin model, assembly process digital twin model and assembly performance digital twin model; (3) The information is exchanged between the assembled digital twin models through knowledge-based interaction and interoperability interfaces. Therefore, the multi-heterogeneous data generated from the assembly processes can be processed and aggregated; (4) For the large amount of data generated during the assembly process, an intelligent algorithm is used to establish an assembly predictability model. Using the Pareto optimal method, a mathematical model of the qualitative mapping relationship between process parameters and assembly quality is developed for an adaptive
With the rapid development of next-generation information technology such as artificial intelligence, industrial Internet, and industrial big data, the era of intelligent manufacturing has come. The digital twin technology has a great potential to be used in the field of product assembly. Based on the findings from reviews, this paper proposes a digital twin-driven assembly-commissioning method for high precision products with multidisciplinary coupling. The main contents are as follows: (1) Through the interaction and integration of data between the virtual space and the physical space of HPPs assembly workshops, a theoretical framework for the digital twin-driven assemblyTable 2 Comparison of main indicators of the two-stage servo valve. Parameters
Traditional assembly technology
Digital twin-driven assembly-commissioning
Primary qualification rate (%) Assembly commissioning time (h) Working pressure (oil port P, A,B) (MPa) Rated oil supply pressure (MPa) Rated flow (pressure difference △P = 7 MPa) (L/min) Amplitude bandwidth (−3 dB) (Hz) Phase bandwidth (−90˚) (Hz) Bias (%) Resolution (%) Degree of asymmetry (%) Non-linearity (%) Hysteresis (%) Temperature drift (−30–150°C) Zero position leakage (L/min)
50 About 8 2∼25 21 5, 10, 20 >100 >100 ≤±2 ≤1 ≤7.5 ≤7.5 ≤4 ≤±4 ≤0.8
90 About 3
≤ ± 1.5 ≤0.7 ≤7.0 ≤7.0 ≤2 ≤ ± 3.6 ≤0.2
13
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optimization of the assembly process.
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The proposed digital twin assembly method has been applied in the assembly process of a high-precision electro-hydraulic servo valve of aerospace type. It has been verified that this method can meet the assembly quality and efficiency requirements of HPPs. However, this research has limitations to be noted and further studied. Firstly, in case study validation, we only validated the military products whose assembly accuracy lower than micron level. Due to the limitation of equipment accuracy, for those products whose assembly accuracy is higher than micron level have not been validated. Additionally, this study mainly concentrates on the product assembly stage. There is no in-depth discussion on the correlation between the upstream and downstream of the assembly processes. Future work is needed for it to be applied throughout the whole life cycle of the products. Acknowledgement This work was supported in part by the National Natural Science Foundation of China (51475301), Shanghai Sailing Program (19YF1401600) and the Fundamental Research Funds for the Central Universities 2232019D3-32. Conflicts of interest None. References [1] Liu Jianhua, Sun Qingchao, Cheng Hui, et al., The State-of-the-art, connotation and developing trends of the products assembly technology, J. Mech. Eng. 54 (11) (2018) 2–28. [2] S Jayaram, U Jayaram, Y Wang, et al., VADE: a virtual assembly design environment, Comput. Graph. Appl. IEEE 19 (6) (1999) 44–50. [3] G Kaufman S, H Wilson R, R Jones, et al., The archimedes 2 mechanical assembly planning system, IEEE International Conference on Robotics & Automation, 1996. [4] Peng Xiang, Liu Zhenyu, Tan Jianrong, et al., Design model simplification for complex products based on changeable correlation analysis, J. Comput.-Aided Des. Comput. Graph. 25 (8) (2013) 1245–1254. [5] Zhou Sihang, Liu Zhenyu, Tan Jianrong, Deviation propagation model of assembly sequence and quality evaluation approach based on degree of dimensional variation, J. Mech. Eng. 47 (2) (2011) 1–8. [6] Zhang Zhixian, Liu Jianhua, Ning Ruxin, Physical assembly process simulation based on Multi-rigid-body dynamics in virtual assembly, J. Mech. Eng. 49 (5) (2013) 90–99. [7] Liu mi, Liu Jianhua, He Yongxi, et al., Research on assembly path planning and optimization of complex structures, J. Mech. Eng. 49 (9) (2013) 97–105. [8] Liu Jiashun, Liu Jianhua, Wang Zhibin, et al., Integrated information model of complex cable harness in virtual environment, Comput. Integr. Manuf. Syst. 19 (5) (2013) 964–971. [9] Hu Wei, Liu Jianhua, Jiang Ke, et al., Assembly precision prediction method for spacecraft based on 3D model, Comput. Integr. Manuf. Syst. 19 (5) (2013) 990–999. [10] Chen Zhehan, Du Fuzhou, Tang Xiaoging, Key measurement characteristics based inspection data modeling for aircraft assembly, Acta Aeronautica et Astronautica Sinica 33 (11) (2012) 2143–2152. [11] Yang Fan, Tang Xiaoqing, Duan Guijiang, Searching model of change propagation paths for mechanical product based on characteristic linkage network, J. Mech. Eng. 47 (19) (2011) 97–106. [12] Du Fuzhou, Chen Zhehan, Tang Xiaoqing, Precision analysis of iGPS measurement field and its application, Acta Aeronautica et Astronautica Sinica 33 (9) (2012) 1737–1745. [13] Zhen Xijin, Wu Dianlian, Fan Xiumin, et al., Distributed parallel collaborative virtual assembly system for complex products, Comput. Integr. Manuf. Syst. 14 (10) (2008) 1990–1995. [14] Qiu Shiguang, Fan Xiumin, Wu Dianliang, et al., Virtual human modeling for interactive assembly and disassembly operation in virtual reality environment, Int. J.
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