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A welding task data model for intelligent process planning of robotic welding
Robotics and Computer Integrated Manufacturing 64 (2020) 101934
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Robotics and Computer Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim
Full Length Article
A welding task data model for intelligent process planning of robotic welding
T
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Weidong Shena,b, Tianliang Hua,b,c, , Chengrui Zhanga,b, Yingxin Yea,b, Zhengyu Lia,b a
School of Mechanical Engineering, Shandong University, Jinan 250061, PR China Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, PR China c Suzhou Institute of Shandong University, Suzhou 215123, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Intelligent process planning Welding task Robotic welding Information integration
Nowadays, as an efficient and automatic welding machine that accepts and executes human instructions, welding robots are widely used in industry. However, the lack of intelligence in process planning makes welding preparation complex and time-consuming. In order to realize intelligent process planning of robotic welding, one of the key factors is designing a welding task data model that can support process planning. However, current welding task models have some drawbacks, such as inaccurate geometry information, lacking information on welding requirements, and lacking consideration of machine-readability and compatibility. They cannot provide sufficient information for intelligent process planning. In this paper, a welding task data model, which includes information on accurate geometry, dimension and welding requirement, is presented to solve these problems. Firstly, through requirement analysis, necessary information items of a welding task data model are analyzed and summarized. Then the welding task data model is designed in detail by using EXPRESS. The feasibility of proposed welding task data model is demonstrated through creating the welding task file of an automobile front door subassembly. Moreover, an application framework of the welding task file is presented. Results show that proposed welding task data model is feasible for supporting intelligent process planning and information integration of robotic welding.
1. Introduction Welding robots play an important role in manufacturing system and provide an automatic, adaptable and efficient pattern for welding production [1]. However, process planning of welding robots is still a time-consuming procedure, and relies on professional technicians who have experienced welding knowledge. It takes a long time and is errorprone to plan welding process artificially for product manufactured in single-piece. Artificial method cannot fulfill requirements of multivariety and small-batch production and highly depends on process planning engineer. Therefore, it is crucial to make process planning of welding robots more intelligent and efficient, which would enable welding robots for self-decision-making in welding process planning, reduce welding preparation time and ensure welding quality. Developing an intelligent process planning system is an effective way to solve above-mentioned problems. The basis input of process planning with an intelligent process planning system is welding task. Therefore, a welding task model with comprehensive and accurate description, which can provide enough information for the intelligent
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process planning system, is a prerequisite for intelligent process planning. At present, the methods of describing welding tasks include 2D drawing and digital information model. Due to the advantages of computer processing and network transmission, digital information model is a better choice for welding tasks of intelligent process planning and is widely researched. At present, digital information models of welding tasks include object-oriented model [2], model in form of formalized symbols [3] and ontological model [4]. These digital information models of welding tasks are based on welding features that refer to concept of machining features. However, a common problem of these models is the lack of compatibility and extensibility. This problem also exists in the NC machining filed, especially for metal cutting. ISO 14649 Standard for Product Model Data Exchange for Numerical Control (STEP-NC) is researched and has been published aiming at solving this problem. STEP-NC is considered as basis of developing next generation of computer numerical control (CNC) machine tools [5], which provides a machining feature-oriented data model for process planning [6]. With the support of machining feature-oriented data model, STEP-NC can be used for developing adaptive
Corrsponding author. E-mail address: [email protected] (T. Hu).
https://doi.org/10.1016/j.rcim.2020.101934 Received 17 September 2019; Received in revised form 7 December 2019; Accepted 5 January 2020 0736-5845/ © 2020 Elsevier Ltd. All rights reserved.
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Fig. 1. Process planning of robotic welding based on different welding task description methods.
process planning of robotic welding. The main contributions of our study are as follow.
manufacturing system [7], on-line inspection system in closed-loop machining [8], STEP-compliant computer-aided design (CAD)/computer-aided manufacturing (CAM) system [9], and intelligent process planning system of CNC machine tools [10–14]. Based on STEP-based machining data model [13], knowledge generation mechanism of machining process planning [10], CNC machining process knowledge base [12], intelligent process planning method [11], and intelligent CNC controller using cloud knowledge base [14], as well as intelligent process planning system of CNC machine tools were developed and implemented in our previous works. Although STEP-NC has started a new era for CNC machining, other manufacturing technologies, especially robotic welding, are rarely affected by this advanced concept. Nowadays methods of describing welding tasks of robotic welding mainly focus on defining the geometry information of welding tasks. Because of the lack of welding requirements, materials and management information, these methods cannot support welding robots to make process decisions. Therefore, a compatible and extensible welding task data model, which include enough information and can be freely switched-over among different software platforms, is urgently needed to fulfill the requirements of intelligent
(1) The intelligent process planning methods and task data models of robotic welding are reviewed. Current problems of welding task data models are summarized. (2) A welding task data model, which consists of geometry, material, welding requirements and management information, is developed to support intelligent process planning of robotic welding. (3) The welding task data model is designed in detail by using objectoriented language EXPRESS, which enables seamless information integration of CAD/CAPP/CAM. The remainder of this paper is organized as follows. Research on intelligent process planning and welding task data model of robotic welding are reviewed in Section 2. Requirements analysis and the structure of the proposed welding task data model are presented in Section 3. Then the welding task data model is designed in detail in Section 4. A case study is implemented in Section 5. Section 6 makes a conclusion of this paper. 2
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Robotic arc welding Robotic arc welding Welding process planning system Robotic remote-laser-welding Robotic arc welding Robotic welding Robotic remote-laser-welding Robot off-line programming system Process planning of remote laser welding Robotic welding Intelligent process planning of robotic welding
Application
2.1. Research on intelligent process planning of robotic welding Although high-accuracy welding production has been possible, process planning of robotic welding still depends on abilities of process planners. In order to improve efficiency and intelligence, process planning of robotic welding is expected to integrate knowledge of human experts. An effective way for solving above-mentioned problem is to combine machine learning algorithm or expert system with process planning of robotic welding. For instance, a method consisted of neural network and multiple regression method for predicting process parameters of robotic arc welding was presented by Kim et al. [15]. An arc welding expert system which included case-based reasoning and heuristic rule-based reasoning, was proposed by Peng et al. [16]. A knowledge database of welding parameters for robotic welding was presented by Yan et al., and used to predict bead shapes [17]. A predicting method of welding parameters which was based on Gaussian process regression and Bayesian optimization algorithm, was proposed by Dong et al. [18]. These methods are used in off-line process planning system, and need human experts to input welding tasks into CAPP system manually. However, multi-variety and small-batch production requires intelligent welding robots, which have abilities of on-line process planning and reading welding tasks automatically to reduce time in process planning. Another way of realizing intelligent process planning of robotic welding is to transfer knowledge of human welders into welding robots [19]. For instance, a remote welding control scheme which enabled knowledge of human welder to be transferred to a welding robot, was presented by Liu et al. [20]. An adaptive neuro-fuzzy inference systembased classifier which was used to rate skill of welder and transfer intelligence of welder into welding robot, was proposed by Liu et al. [21]. In the research, knowledge of human welder was transferred into welding robot through model trained by experimental data. Controller of the welding robot was able to control welding process with different welding currents by using the model. However, this method cannot make full use of welding cases and professional knowledge of human experts. In addition, because of lacking a system that can read welding tasks, welding robot cannot achieve intelligent process planning of complex welding tasks. Therefore, no matter which way is adopted, a welding task model is essential for intelligent process planning. As the basis of welding process planning, a welding task model provides necessary information for process planning. The information should contain materials, dimensions, welding requirements and geometry, which directly affects the selection of welding methods and parameters. However, there is a lack of welding task data model, which supports process planning of robotic welding. It causes that intelligent process planning system lacks detailed and complete information input. Therefore, the system cannot achieve reliable and efficient process planning. Quality of welding process outputted from the system cannot be ensured neither.
√ Rubinovitz et al. [22] Buchal et al. [23] Maropoulos [2] Reinhart et al. [24] Liu et al. [3] Tuovinen et al. [25] Erdős et al. [26] Xiao et al. [27] Erdős et al. [28] Kuss et al. [4] 1988 1989 2000 2008 2010 2010 2013 2014 2016 2017
√ √
√ √ √ √ √ √ √ √ √ √ √ √ √ Using lines/curves to describe seams Using a series of closely points to describe seams Developing a feature-based, object-oriented task model AR-interface based welding task description Defining a feature-based task model using format language Defining an object-oriented task description document Defining welding task based on CAD model Using STEP framework to define robot tasks Defining welding task based on workpiece geometry A ontological welding task description based on welding features Our method
STEP XML STEP XML XML STEP
Data format Management Included information items Geometry Material Requirement Methods Authors Year
Table 1 Different methods for welding task description.
2. Research background
2.2. Research on welding task data model As described in the left side of Fig. 1, welding task information is defined through 2D drawing of product in traditional process planning methods of robotic welding. The methods require artificial involvement, e.g. interpretation of the 2D drawing, planning of welding parameters, programming of welding robot. Furthermore, welding information integration and reuse of welding cases are not taken into consideration in the methods. However, if there were a digital welding task data model, process planning of robotic welding would be more intelligent and efficient, as described in the right side of Fig. 1. With the support of a digital welding task data model, necessary information for intelligent process 3
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Fig. 2. Information requirements for welding task data model.
Fig. 3. Overall structure of task data model of welding robot.
these welding task data models lack welding requirements information and material information, which are essential for process planning of robotic welding. In order to quickly define operations of welding robots on a task-level, a welding task data model based on seam elements which consisted of two or more points, was proposed by Reinhart et al. [24]. However, it is not accurate that geometric constraints of weld seams are described by two or more points. An accurate method for describing welding task data model was presented by Liu et al. [3]. In the research, welding features for arc welding process were defined in form of customized and formalized symbols. Ontological manufacturing task model of robotic welding based on continuous welding features was presented by Kuss et al. [4]. However, these welding task data models lack compatibility and extensibility. Thus a welding robot manufacturing task model which contained top-level entities of STEP-NC was proposed by Xiao et al. with using EXPRESS-G [27]. A task description document containing information of objects and quality criteria, was defined by Tuovinen et al. [25]. A welding task description method based on the CAD model of workpiece were defined by Erdős et al. [26]. A definition method of welding tasks comprised workpiece, fixture, and workcell geometries was proposed by Erdős et al. [28]. However, these welding task data models lack necessary information for process planning of robotic welding. Through above-mentioned literature reviews, problems of welding task data model for intelligent process planning of robotic welding are
Fig. 4. EXPRESS representation of WeldTask.
planning can be read directly by CAPP and CAM software platforms to carry out intelligent process planning, path planning based on feature mapping, and post-processing of process plan. Information integration of welding tasks, welding process planning and welding robot programming can be realized. Furthermore, welding cases can be stored and reused to improve intelligence of welding robots. As shown in Table 1, different methods for welding task description were proposed to support intelligent process planning. A welding task data model which was composed of solid model of welded parts, and lines/curves describing seams, was presented by J. Rubinovitz and R.A. Wysk [22]. The seams to be welded on workpiece were defined as space curves in [23]. A feature-based and object-oriented welding task data model, which was used in early design analysis system, was proposed by Maropoulos et al. [2]. In the research, welding feature was presented to define basic shapes, which had certain welding process. However,
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Fig. 5. EXPRESS-G representation of WeldTask.
3. Architecture of welding task data model 3.1. Requirements analysis In order to support intelligent process planning of robotic welding, a welding task data model must contain sufficient information used as input of process planning. As described in Fig. 2, information requirements for the welding task data model are summarized as follows. (1) Geometry information. Accurate geometry information of welding feature is essential for selecting welding methods and appropriate welding parameters. Furthermore, path planning of welding task needs information of geometry elements (e.g. points, edges, normal, face, etc.) of workpiece to constraint motion of welding gun. (2) Material information. The type and property of workpiece material directly affect the selection of welding methods and determination of welding parameters such as welding speed, current, voltage, etc. Thus, material information should be included in welding task data model. (3) Welding requirements. With consideration of different welding requirements such as welding quality, imperfections, tolerances, etc., optimal welding parameters need to be selected to fulfill design requirements of product. (4) Management information. In order to facilitate retrieval, modification and reuse of welding tasks, it is necessary to add management information such as creator, time and identifier of welding task.
Fig. 6. EXPRESS representation of Weld_feature.
summarized as follows. (1) Current welding task data models focus on describing simple geometry information of welding feature, and lack necessary information, e.g. welding requirements information, accurate geometric information and material information, for intelligent process planning of robotic welding. (2) Due to a lack of compatibility and extensibility, welding task data cannot be exchanged among different software platforms (e.g. CAD, CAPP, CAM) and applied in different welding types, e.g. spot welding, arc welding, etc. In order to solve the two problems, a welding task data model which contains sufficient information for intelligent process planning of robotic welding, and has the ability of sharing geometry entities of STEP, is presented and implemented by using EXPRESS and EXPRESS-G in this paper.
Furthermore, intelligent process planning of robotic welding and seamless information integration of CAD/CAPP/CAM for welding robot also require an appropriate welding task data format. Requirements for 5
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Fig. 7. EXPRESS-G representation of Weld_feature.
3.2. Structure of welding task data model In order to fulfill above-mentioned information requirements, a welding task data model for intelligent process planning is presented, as described in Fig. 3. The welding task data model consists of management information, weld feature information, and components information. Weld feature information contains joint information, weld information and welding requirements used to describe welding quality, imperfections and tolerances. Components information contains material information and geometry information of workpieces. The structure of the welding task data model is described in detail as follows.
• Weld task is the super class of all other elements in the welding task • •
Fig. 8. EXPRESS representation of Joint_type.
• •
the welding task data format are summarized as follows. (1) Machine-readable. Welding task data should have the ability to be directly read by machines without artificial interpretation, in order to improve intelligence and efficiency of process planning. (2) Compatible. In order to realize information integration, welding task data format should be compatible with different software such as CAD, CAPP and CAM. (3) Extensible. For more complex welding task, welding task data model should be extended easily to describe various welding information.
•
To fulfill above-mentioned requirements, the structure and detailed design of a welding task data model are presented and stated in Section 3.2 and Section 4, respectively.
•
6
data model, and represents the abstract entity of welding task information. Management information is to describe information, such as designers, task identifier, approver, etc., which are used to retrieve, modify and reuse welding task. Weld feature information is a key element of the welding task data model, and contains information of joint, weld, and welding requirements. It directly affects process planning of robotic welding. Joint information contains geometry information and groove information which is to describe types and dimension of groove. Weld information is to describe path, types, dimension of weld. Path of weld is used to plan motion path of welding robot. Types and dimension of weld directly affect selection of welding methods and welding parameters. Welding requirements is to describe requirements for welding quality, imperfections and tolerances. Welding quality includes welding quality standard and grades determined by designer. Imperfections include undercut, gas hole, etc., which are used to evaluate welding quality. Due to possible deformation of workpiece caused by welding heat, tolerances requirements include tolerances of linear dimensions, angular dimensions, straightness, etc. Components information includes material information and geometry information of components to be welded together. Geometry of the components can be tube or sheet. Due to different weld ability of materials, materials types of the components directly affect selection of welding methods and welding parameters.
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Fig. 9. EXPRESS-G representation of Joint_type.
family which is used to enable all information of a product to be exchanged among different software [29]. EXPRESS can be used in two forms. One is a formal language that is based on lexical notation and syntax [30]. The other is a graphical form called EXPRESS-G, which provides a compact and rich illustration [31]. Although EXPRESS is not a programming language, but it is a powerful tool to describe types defined by users. Advantages of EXPRESS include object-oriented characteristic, textual schema, and readability. As a language recommended in STEP, EXPRESS is supported widely by various software tools for product design and process planning. Therefore, EXPRESS-G diagram and EXPRESS language are adopted to design the welding task data model for robotic welding in detail. 4.2. Welding task data model Based on requirements analysis in Section 3.1, schema named Weld_task _model is designed by using EXPRESS to fulfill the information requirements, as described in Fig. 4. Entity named WeldTask is the toplevel entity of the schema. As depicted in Fig. 5, three main attributes of WeldTask are Its_management_info, Its_components and Its_weld_features, used to describe management information, component information and welding feature information, respectively. Attributes of entity named Management_info contain Its_id, Its_author, Its_time, etc. Entity named Component is used to describe information of identifier, material and geometry of workpieces to be welded together. Entity named Weld_feature is used to describe joint information, weld information and welding requirement, and designed in detail in Section 4.3.
Fig. 10. EXPRESS representation of Weld_type.
• Geometry information represents topology and dimensions information of components to be welded. Geometry elements such as points, edges, normal, face, etc., are used to describe geometry constraints of weld features.
Based on the structure of the welding task data model, which includes essential information items for process planning and information integration of robotic welding, the welding task data model is designed in detail in Section 4. 4. Detailed design of welding task data model
4.3. Welding feature 4.1. Language for describing model As a key entity of the welding task data model, welding features are used to describe welding requirements, geometry and dimensions of welded area of a workpiece. Entity named Weld_feature is defined as follows by using EXPRESS, as described in Fig. 6. EXPRESS-G
As a powerful and easy-to-use language, EXPRESS is selected to describe complex structure and entities of the welding task data model. EXPRESS is developed in Standard of Exchange Product data (STEP) 7
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Fig. 11. EXPRESS-G representation of Weld_type.
Curve_feature. Their common attributes contain Its_id, Connected_parts, Its_joint, Its_weld, and Welding_requirement. Attribute called Connected_parts is used to specify workpieces to be welded together. Subtypes of entity called Joint_type, which is designed in detail in Section 4.4, include Butt_joint, Edge_joint, Tee_joint, Corner_joint, Lap_joint, etc. Entity named Weld_type, which is designed in detail in Section 4.5, has subtypes named Butt_weld, Edge_weld, Plug_weld, Fillet_weld, etc. Entity named Requirement is designed in detail in Section 4.6. 4.4. Joint Entity named Joint_type is used to describe information of joint type and groove type of welding feature. Joint_type is defined as follows by using EXPRESS, as described in Fig. 8. EXPRESS-G representation of Joint_type is depicted in Fig. 9. Subtypes of Joint_type include Butt_joint, Edge_joint, Tee_joint, Corner_joint, Lap_joint, etc. Attribute named Its_groove is used to describe type and dimension of groove that is classified into V_groove, J_groove, U_groove, I_groove, etc. Geometry information of joint is defined by sharing geometry entities of STEP, e.g. plane, cylindrical surface, B-spline surface, etc. Geometry attributes of Tee_joint contain Wall_surface, Base_surface. Attributes of Lap_joint include Top_surface and Bottom_surface.
Fig. 12. EXPRESS representation of welding requirement.
representation of Weld_feature is depicted in Fig. 7. Weld_feature has four subtypes named Spot_feature, Linear_feature, Circular_feature and
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Fig. 13. EXPRESS-G representation of welding requirement.
Fig. 14. Welding tasks of front door subassembly of an automobile.
As an important attribute of Weld_type, Its_path is used to describe geometry information of weld. Four subtypes of entity named Path include Point, Line, Circle and B_spline_curve, which refer to STEP. Path has two optional attributes called Its_start_point and Its_end_point that are used to describe position of start point and end point, respectively.
4.5. Weld type Entity named Weld_type is used to describe path information and dimension information of weld of welding feature. Weld_type is designed as follows by using EXPRESS, as described in Fig. 10. EXPRESS-G representation of Weld_type is depicted in Fig. 11. Subtypes of Weld_type include Butt_weld, Edge_weld, Plug_weld, Fillet_weld, Spot_weld, etc. Attributes of Spot_weld contain Spot_weld_diameter, Distance_between_welds and Number_of_welds. Optional attributes of Fillet_weld include Throat_thickness, Leg_length, Number_of_WE, etc. Number_of_WE is used to describe number of weld elements in welding feature.
4.6. Welding requirement Entity named Requirement is used to describe requirements for welding quality, imperfections and tolerances of welding feature. Requirement is defined as follows by using EXPRESS, as described in 9
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welding feature 1 is shown in Fig. 17. It is created manually in format of Part 21.
• The geometry information of the welding task is defined in lines • • •
Fig. 15. Geometry of welding task 1.
#15, #16, and #39. The geometry of top_surface and bottom_surface is defined in lines #15 and #16 respectively. The path of weld is defined in line #39. The material information of the welded components, i.e. part 1 and part 4, is defined in lines #17 and #51. The materials of part 1 and part 4 are SPC270C metal sheet with thickness of 0.7 mm. The requirements of the welding task are defined in lines #11, #18, and #132. The welding quality is required to be class B. The maximum allowable dimension of undercut is 0. The maximum allowable quantity of gas holes is 0. The management information about the welding task is defined in line #2. The creator, time and identifier of the welding task are described in line #2.
As the input of intelligent process planning system, the created welding task file can be used in CAPP that includes expert system and intelligent algorithm. An application framework of the welding task file is described in Fig. 18. The welding task file created in CAD is firstly transmitted to CAPP for process planning of robotic welding. After the welding task file is read by CAPP software with an interpreter, the Part 21 file is parsed into an intermediate file that can be understood by machine. Based on the information parsed from the welding task file, appropriate process plan, which includes welding path and welding parameters, is output by CAPP. OLP software can generate and simulate robotic program in accordance with the welding task file and the process plan. Then the robotic program is transmitted to welding robot controller. Finally, the results of welding operation are fed back to CAD, CAPP, and OLP system. A closed-loop iterative optimization of welding process plan can be carried out in the proposed application framework.
Fig. 16. Geometry of welding feature 1.
Fig. 12. EXPRESS-G representation of Requirement is depicted in Fig. 13. Optional attributes of Requirement contain Its_weld_quality, Its_imperfections and Its_tolerances. Weld_quality is used to specify welding quality standard and grade of welding task. Imperfections is used to determine maximum allowable value of imperfections which include under cut, gas hole, shrinkage of weld root, etc. Tolerances is to describe tolerances of dimension and shape of welding feature after welding. Optional attributes of Tolerances include Linear_dimensions, Angular_dimensions and Straightness, which are to specify tolerances of linear dimensions, angular dimensions and straightness, respectively. Definitions of tolerance grades refer to ISO 13920.
5.2. Evaluation (1) Information integrity In terms of information integrity, the proposed welding task data model includes more information items than other welding task data models, as shown in Table 1. Information of geometry, material, requirements and management of welding tasks is defined in the proposed welding task data model. The information is essential for intelligent process planning of robotic welding. Geometry information is used to plan optimal welding path. Information of material and requirements is necessary for selecting appropriate welding method and welding parameters. Management information plays an important role in reuse of welding tasks and iterative optimization of welding process plans.
5. Case study 5.1. Welding tasks of front door subassembly In order to demonstrate the feasibility of the proposed welding task data model, a case study about the front door subassembly of an automobile is implemented. As shown in Fig. 14, the front door subassembly of an automobile consists of four metal sheets, which are part 1, part 2, part 3, and part 4. There are four welding tasks to be completed by welding robots. Welding task 1 is designed to connect part 1 and part 4. The geometry of welding task 1 is shown in Fig. 15. Welding task 1 consists of three welding features, i.e. welding feature 1, welding feature 2, and welding feature 3. P1, P2, P3, and P4 refer to the start point or the end point of welding seams. The geometry of welding feature 1 is shown in Fig. 16. Here, the top_surface and the bottom_surface are two B_spline_surface. The curve_weld_part_4 and the curve_weld_part_1 are two B_spline_curve. The thickness of part 4 is h (h = 0.7 mm). The gap between the top_surface and the bottom_surface is g (g = 2 mm). P1 and P2 are the start point and the end point of welding seam respectively. The task data of welding feature 1 is defined based on the proposed welding task data model. The file format of the task data is an extension of ISO 10303-21 (Part 21) specifications. The welding task file of
(2) Compatibility and effectiveness The proposed welding task data model is implemented in format of Part 21, as shown in Fig. 17. Due to cross-platform characteristics of Part 21 format, information integration among CAD/CAPP/CAM for robotic welding can be realized. The case study shows the correctness of the proposed welding task data model by creating a task file in Part 21 format. Compared to traditional process planning systems that need input additional information described in 2D drawing manually, the efficiency of process planning can be improved by using the proposed welding task data model. As shown in Table 2, qualitative efficiency comparisons of process planning by using different welding task definition methods are analyzed. With respect to time spent in creating welding tasks, it needs more time to generate traditional 2D drawing because of the extra conversion from 3D to 2D. Conversely, welding 10
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Fig. 17. Task data of welding feature 1.
Fig. 18. Application framework of a welding task data file.
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Table 2 Qualitative view of efficiency of process planning systems with different task definition methods. Task definition methods
Time spent in creating welding task
Time spent in transmitting task
Time spent in inputting task into CAPP system
Traditional 2D drawing Proposed welding task data model
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References
tasks can be generated directly from 3D model by using proposed welding task model. Owing to the compatibility of proposed welding task model, the time spent in transmitting welding tasks is less than traditional 2D drawing. Because of the machine-readability of proposed welding task data model, the time spent in inputting welding tasks into CAPP is less than traditional 2D drawing that may require artificial interpretation and manual input.
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Technol. 94 (9-12) (2018) 3413–3425. [13] W. Zhu, T. Hu, W. Luo, Y. Yang, C. Zhang, A STEP-based machining data model for autonomous process generation of intelligent CNC controller, Int. J. Adv. Manuf. Technol. (2018) 1–15. [14] W. Gao, C. Zhang, T. Hu, Y. Ye, An intelligent CNC controller using cloud knowledge base, Int. J. Adv. Manuf. Technol. (2019) 1–11. [15] I.S. Kim, J.S. Son, C.E. Park, C.W. Lee, Y.K.D.V. Prasad, A study on prediction of bead height in robotic arc welding using a neural network, J. Mater. Process. Technol. 130-131 (2002) 229–234. [16] P. Peng, J. Tian, L. Wu, M. Da, Intelligent hybrid system of welding parameters for robotic arc welding task-level off-line programming, Proc. SPIE Int. Soc. Opt. Eng. 4192 (1) (2000) 440–445. [17] S.J. Yan, H.C. Fang, S.K. Ong, A.Y.C. Nee, Optimal pass planning for robotic welding of large-dimension joints with nonuniform grooves, Proc. Instit. Mech. Eng. Part B 232 (13) (2018) 2386–2397. [18] H. Dong, M. Cong, Y. Zhang, Y. Liu, H. Chen, Modeling and real-time prediction for complex welding process based on weld pool, Int. J. Adv. Manuf. Technol. 96 (5-8) (2018) 2495–2508. [19] Y.K. Liu, Y.M. Zhang, L. Kvidahl, Skilled human welder intelligence modeling and control: Part II-analysis and control applications, Welding J. 93 (5) (2014) 162–170. [20] Y. Liu, Y. Zhang, Toward Welding Robot with human knowledge: a remotely-controlled approach, IEEE Trans. Autom. Sci. Eng. 12 (2) (2015) 769–774. [21] Y.K. Liu, Y.M. Zhang, Supervised learning of human welder behaviors for intelligent robotic welding, IEEE Trans. Autom. Sci. Eng. 14 (3) (2017) 1532–1541. [22] J. Rubinovitz, R.A. Wysk, Task level off-line programming system for robotic arc welding — an overview, J. Manuf. Syst. 7 (4) (1988) 293–306. [23] R.O. Buchal, D.B. Cherchas, F. Sassani, J.P. Duncan, Simulated off-line programming of welding robots, Int. J. Rob. Res. 8 (3) (1989) 31–43. [24] G. Reinhart, U. Munzert, W. Vogl, A programming system for robot-based remote-laserwelding with conventional optics, CIRP Ann.-Manuf. Technol. 57 (1) (2008) 37–40. [25] L. Tuovinen, T. Talus, E. Koponen, P. Laurinen, J. Röning, A task-to-method transformation system for self-configuration in manufacturing, IEEE International Conference on Industrial Informatics (INDIN), 2010, pp. 245–252. [26] G. Erdős, Z. Kemény, A. Kovács, J. Váncza, Planning of remote laser welding processes, Procedia CIRP 7 (2013) 222–227. [27] W. Xiao, J. Huan, S. Dong, A STEP-compliant industrial robot data model for robot offline programming systems, Rob. Comput.-Integr. Manuf. 30 (2) (2014) 114–123. [28] G. Erdős, C. Kardos, Z. Kemény, A. Kovács, J. Váncza, Process planning and offline programming for robotic remote laser welding systems, Int. J. Computer Integr. Manuf. 29 (12) (2016) 1287–1306. [29] ISO 10303-11:2004Industrial automation systems and integration – Product data representation and exchange – Part 11: Description methods: The EXPRESS language reference manual, 2004. [30] D. Kretz, T. Teich, J. Militzer, T. Neumann, Implementing ISO standard 10303 application protocol 224 for automated process planning, Rob. Comput. Integr. Manuf. 27 (4) (2011) 729–734. [31] X.G. Ming, K.L. Mak, J.Q. Yan, A PDES/STEP-based information model for computeraided process planning, Rob. Comput. Integr. Manuf. 14 (5) (1998) 347–361.
6. Conclusions It is obvious that a welding task data model can facilitate the intelligence and efficiency of process planning for robotic welding. However, current welding task data models lack essential information items and compatibility. In the proposed welding task data model, accurate geometry information, material information, requirements information, and management information is defined by using EXPRESS and EXPRESS-G. Due to cross-platform characteristics, Part 21 format is adopted to define welding task data file. In order to demonstrate the correctness of the proposed welding task data model, a case study about the front door subassembly of an automobile is implemented. Welding task data file of a welding feature of the automobile front door subassembly is created. An application framework of welding task data files is presented to show the advantages of welding task data model. The output task file can be used not only for planning process in CAPP but also for programming in OLP. Iterative optimization of welding process plan can be achieved in the proposed application framework. With the support of the proposed welding task data model, information requirements for intelligent process planning of robotic welding can be fulfilled. Due to machine readability and compatibility of the welding task data model, seamless information integration of CAD/CAPP/CAM platforms can be realized. Due to the object-oriented characteristic, the welding task data model can be easily extended and applied to more complex welding tasks. Future work will focus on designing a welding robot controller, which can directly read welding task file, to improve decision-making intelligence of welding robots. Moreover, algorithms of welding feature recognition need to be developed for improving efficiency of defining welding task files. CRediT authorship contribution statement Weidong Shen: Conceptualization, Methodology, Writing - original draft, Software. Tianliang Hu: Conceptualization, Project administration, Writing - review & editing. Chengrui Zhang: Conceptualization, Supervision. Yingxin Ye: Software. Zhengyu Li: Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The work is supported by the National Natural Science Foundation of China (Grant No. 51875323) and Key R&D Program of Shandong Province (Major scientific and technological innovation project) (Grant No. 2019JZZY010123). 12
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Robotics and Computer Integrated Manufacturing journal homepage: www.elsevier.com/locate/rcim
Full Length Article
A welding task data model for intelligent process planning of robotic welding
T
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Weidong Shena,b, Tianliang Hua,b,c, , Chengrui Zhanga,b, Yingxin Yea,b, Zhengyu Lia,b a
School of Mechanical Engineering, Shandong University, Jinan 250061, PR China Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, PR China c Suzhou Institute of Shandong University, Suzhou 215123, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Intelligent process planning Welding task Robotic welding Information integration
Nowadays, as an efficient and automatic welding machine that accepts and executes human instructions, welding robots are widely used in industry. However, the lack of intelligence in process planning makes welding preparation complex and time-consuming. In order to realize intelligent process planning of robotic welding, one of the key factors is designing a welding task data model that can support process planning. However, current welding task models have some drawbacks, such as inaccurate geometry information, lacking information on welding requirements, and lacking consideration of machine-readability and compatibility. They cannot provide sufficient information for intelligent process planning. In this paper, a welding task data model, which includes information on accurate geometry, dimension and welding requirement, is presented to solve these problems. Firstly, through requirement analysis, necessary information items of a welding task data model are analyzed and summarized. Then the welding task data model is designed in detail by using EXPRESS. The feasibility of proposed welding task data model is demonstrated through creating the welding task file of an automobile front door subassembly. Moreover, an application framework of the welding task file is presented. Results show that proposed welding task data model is feasible for supporting intelligent process planning and information integration of robotic welding.
1. Introduction Welding robots play an important role in manufacturing system and provide an automatic, adaptable and efficient pattern for welding production [1]. However, process planning of welding robots is still a time-consuming procedure, and relies on professional technicians who have experienced welding knowledge. It takes a long time and is errorprone to plan welding process artificially for product manufactured in single-piece. Artificial method cannot fulfill requirements of multivariety and small-batch production and highly depends on process planning engineer. Therefore, it is crucial to make process planning of welding robots more intelligent and efficient, which would enable welding robots for self-decision-making in welding process planning, reduce welding preparation time and ensure welding quality. Developing an intelligent process planning system is an effective way to solve above-mentioned problems. The basis input of process planning with an intelligent process planning system is welding task. Therefore, a welding task model with comprehensive and accurate description, which can provide enough information for the intelligent
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process planning system, is a prerequisite for intelligent process planning. At present, the methods of describing welding tasks include 2D drawing and digital information model. Due to the advantages of computer processing and network transmission, digital information model is a better choice for welding tasks of intelligent process planning and is widely researched. At present, digital information models of welding tasks include object-oriented model [2], model in form of formalized symbols [3] and ontological model [4]. These digital information models of welding tasks are based on welding features that refer to concept of machining features. However, a common problem of these models is the lack of compatibility and extensibility. This problem also exists in the NC machining filed, especially for metal cutting. ISO 14649 Standard for Product Model Data Exchange for Numerical Control (STEP-NC) is researched and has been published aiming at solving this problem. STEP-NC is considered as basis of developing next generation of computer numerical control (CNC) machine tools [5], which provides a machining feature-oriented data model for process planning [6]. With the support of machining feature-oriented data model, STEP-NC can be used for developing adaptive
Corrsponding author. E-mail address: [email protected] (T. Hu).
https://doi.org/10.1016/j.rcim.2020.101934 Received 17 September 2019; Received in revised form 7 December 2019; Accepted 5 January 2020 0736-5845/ © 2020 Elsevier Ltd. All rights reserved.
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Fig. 1. Process planning of robotic welding based on different welding task description methods.
process planning of robotic welding. The main contributions of our study are as follow.
manufacturing system [7], on-line inspection system in closed-loop machining [8], STEP-compliant computer-aided design (CAD)/computer-aided manufacturing (CAM) system [9], and intelligent process planning system of CNC machine tools [10–14]. Based on STEP-based machining data model [13], knowledge generation mechanism of machining process planning [10], CNC machining process knowledge base [12], intelligent process planning method [11], and intelligent CNC controller using cloud knowledge base [14], as well as intelligent process planning system of CNC machine tools were developed and implemented in our previous works. Although STEP-NC has started a new era for CNC machining, other manufacturing technologies, especially robotic welding, are rarely affected by this advanced concept. Nowadays methods of describing welding tasks of robotic welding mainly focus on defining the geometry information of welding tasks. Because of the lack of welding requirements, materials and management information, these methods cannot support welding robots to make process decisions. Therefore, a compatible and extensible welding task data model, which include enough information and can be freely switched-over among different software platforms, is urgently needed to fulfill the requirements of intelligent
(1) The intelligent process planning methods and task data models of robotic welding are reviewed. Current problems of welding task data models are summarized. (2) A welding task data model, which consists of geometry, material, welding requirements and management information, is developed to support intelligent process planning of robotic welding. (3) The welding task data model is designed in detail by using objectoriented language EXPRESS, which enables seamless information integration of CAD/CAPP/CAM. The remainder of this paper is organized as follows. Research on intelligent process planning and welding task data model of robotic welding are reviewed in Section 2. Requirements analysis and the structure of the proposed welding task data model are presented in Section 3. Then the welding task data model is designed in detail in Section 4. A case study is implemented in Section 5. Section 6 makes a conclusion of this paper. 2
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Robotic arc welding Robotic arc welding Welding process planning system Robotic remote-laser-welding Robotic arc welding Robotic welding Robotic remote-laser-welding Robot off-line programming system Process planning of remote laser welding Robotic welding Intelligent process planning of robotic welding
Application
2.1. Research on intelligent process planning of robotic welding Although high-accuracy welding production has been possible, process planning of robotic welding still depends on abilities of process planners. In order to improve efficiency and intelligence, process planning of robotic welding is expected to integrate knowledge of human experts. An effective way for solving above-mentioned problem is to combine machine learning algorithm or expert system with process planning of robotic welding. For instance, a method consisted of neural network and multiple regression method for predicting process parameters of robotic arc welding was presented by Kim et al. [15]. An arc welding expert system which included case-based reasoning and heuristic rule-based reasoning, was proposed by Peng et al. [16]. A knowledge database of welding parameters for robotic welding was presented by Yan et al., and used to predict bead shapes [17]. A predicting method of welding parameters which was based on Gaussian process regression and Bayesian optimization algorithm, was proposed by Dong et al. [18]. These methods are used in off-line process planning system, and need human experts to input welding tasks into CAPP system manually. However, multi-variety and small-batch production requires intelligent welding robots, which have abilities of on-line process planning and reading welding tasks automatically to reduce time in process planning. Another way of realizing intelligent process planning of robotic welding is to transfer knowledge of human welders into welding robots [19]. For instance, a remote welding control scheme which enabled knowledge of human welder to be transferred to a welding robot, was presented by Liu et al. [20]. An adaptive neuro-fuzzy inference systembased classifier which was used to rate skill of welder and transfer intelligence of welder into welding robot, was proposed by Liu et al. [21]. In the research, knowledge of human welder was transferred into welding robot through model trained by experimental data. Controller of the welding robot was able to control welding process with different welding currents by using the model. However, this method cannot make full use of welding cases and professional knowledge of human experts. In addition, because of lacking a system that can read welding tasks, welding robot cannot achieve intelligent process planning of complex welding tasks. Therefore, no matter which way is adopted, a welding task model is essential for intelligent process planning. As the basis of welding process planning, a welding task model provides necessary information for process planning. The information should contain materials, dimensions, welding requirements and geometry, which directly affects the selection of welding methods and parameters. However, there is a lack of welding task data model, which supports process planning of robotic welding. It causes that intelligent process planning system lacks detailed and complete information input. Therefore, the system cannot achieve reliable and efficient process planning. Quality of welding process outputted from the system cannot be ensured neither.
√ Rubinovitz et al. [22] Buchal et al. [23] Maropoulos [2] Reinhart et al. [24] Liu et al. [3] Tuovinen et al. [25] Erdős et al. [26] Xiao et al. [27] Erdős et al. [28] Kuss et al. [4] 1988 1989 2000 2008 2010 2010 2013 2014 2016 2017
√ √
√ √ √ √ √ √ √ √ √ √ √ √ √ Using lines/curves to describe seams Using a series of closely points to describe seams Developing a feature-based, object-oriented task model AR-interface based welding task description Defining a feature-based task model using format language Defining an object-oriented task description document Defining welding task based on CAD model Using STEP framework to define robot tasks Defining welding task based on workpiece geometry A ontological welding task description based on welding features Our method
STEP XML STEP XML XML STEP
Data format Management Included information items Geometry Material Requirement Methods Authors Year
Table 1 Different methods for welding task description.
2. Research background
2.2. Research on welding task data model As described in the left side of Fig. 1, welding task information is defined through 2D drawing of product in traditional process planning methods of robotic welding. The methods require artificial involvement, e.g. interpretation of the 2D drawing, planning of welding parameters, programming of welding robot. Furthermore, welding information integration and reuse of welding cases are not taken into consideration in the methods. However, if there were a digital welding task data model, process planning of robotic welding would be more intelligent and efficient, as described in the right side of Fig. 1. With the support of a digital welding task data model, necessary information for intelligent process 3
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Fig. 2. Information requirements for welding task data model.
Fig. 3. Overall structure of task data model of welding robot.
these welding task data models lack welding requirements information and material information, which are essential for process planning of robotic welding. In order to quickly define operations of welding robots on a task-level, a welding task data model based on seam elements which consisted of two or more points, was proposed by Reinhart et al. [24]. However, it is not accurate that geometric constraints of weld seams are described by two or more points. An accurate method for describing welding task data model was presented by Liu et al. [3]. In the research, welding features for arc welding process were defined in form of customized and formalized symbols. Ontological manufacturing task model of robotic welding based on continuous welding features was presented by Kuss et al. [4]. However, these welding task data models lack compatibility and extensibility. Thus a welding robot manufacturing task model which contained top-level entities of STEP-NC was proposed by Xiao et al. with using EXPRESS-G [27]. A task description document containing information of objects and quality criteria, was defined by Tuovinen et al. [25]. A welding task description method based on the CAD model of workpiece were defined by Erdős et al. [26]. A definition method of welding tasks comprised workpiece, fixture, and workcell geometries was proposed by Erdős et al. [28]. However, these welding task data models lack necessary information for process planning of robotic welding. Through above-mentioned literature reviews, problems of welding task data model for intelligent process planning of robotic welding are
Fig. 4. EXPRESS representation of WeldTask.
planning can be read directly by CAPP and CAM software platforms to carry out intelligent process planning, path planning based on feature mapping, and post-processing of process plan. Information integration of welding tasks, welding process planning and welding robot programming can be realized. Furthermore, welding cases can be stored and reused to improve intelligence of welding robots. As shown in Table 1, different methods for welding task description were proposed to support intelligent process planning. A welding task data model which was composed of solid model of welded parts, and lines/curves describing seams, was presented by J. Rubinovitz and R.A. Wysk [22]. The seams to be welded on workpiece were defined as space curves in [23]. A feature-based and object-oriented welding task data model, which was used in early design analysis system, was proposed by Maropoulos et al. [2]. In the research, welding feature was presented to define basic shapes, which had certain welding process. However,
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Fig. 5. EXPRESS-G representation of WeldTask.
3. Architecture of welding task data model 3.1. Requirements analysis In order to support intelligent process planning of robotic welding, a welding task data model must contain sufficient information used as input of process planning. As described in Fig. 2, information requirements for the welding task data model are summarized as follows. (1) Geometry information. Accurate geometry information of welding feature is essential for selecting welding methods and appropriate welding parameters. Furthermore, path planning of welding task needs information of geometry elements (e.g. points, edges, normal, face, etc.) of workpiece to constraint motion of welding gun. (2) Material information. The type and property of workpiece material directly affect the selection of welding methods and determination of welding parameters such as welding speed, current, voltage, etc. Thus, material information should be included in welding task data model. (3) Welding requirements. With consideration of different welding requirements such as welding quality, imperfections, tolerances, etc., optimal welding parameters need to be selected to fulfill design requirements of product. (4) Management information. In order to facilitate retrieval, modification and reuse of welding tasks, it is necessary to add management information such as creator, time and identifier of welding task.
Fig. 6. EXPRESS representation of Weld_feature.
summarized as follows. (1) Current welding task data models focus on describing simple geometry information of welding feature, and lack necessary information, e.g. welding requirements information, accurate geometric information and material information, for intelligent process planning of robotic welding. (2) Due to a lack of compatibility and extensibility, welding task data cannot be exchanged among different software platforms (e.g. CAD, CAPP, CAM) and applied in different welding types, e.g. spot welding, arc welding, etc. In order to solve the two problems, a welding task data model which contains sufficient information for intelligent process planning of robotic welding, and has the ability of sharing geometry entities of STEP, is presented and implemented by using EXPRESS and EXPRESS-G in this paper.
Furthermore, intelligent process planning of robotic welding and seamless information integration of CAD/CAPP/CAM for welding robot also require an appropriate welding task data format. Requirements for 5
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Fig. 7. EXPRESS-G representation of Weld_feature.
3.2. Structure of welding task data model In order to fulfill above-mentioned information requirements, a welding task data model for intelligent process planning is presented, as described in Fig. 3. The welding task data model consists of management information, weld feature information, and components information. Weld feature information contains joint information, weld information and welding requirements used to describe welding quality, imperfections and tolerances. Components information contains material information and geometry information of workpieces. The structure of the welding task data model is described in detail as follows.
• Weld task is the super class of all other elements in the welding task • •
Fig. 8. EXPRESS representation of Joint_type.
• •
the welding task data format are summarized as follows. (1) Machine-readable. Welding task data should have the ability to be directly read by machines without artificial interpretation, in order to improve intelligence and efficiency of process planning. (2) Compatible. In order to realize information integration, welding task data format should be compatible with different software such as CAD, CAPP and CAM. (3) Extensible. For more complex welding task, welding task data model should be extended easily to describe various welding information.
•
To fulfill above-mentioned requirements, the structure and detailed design of a welding task data model are presented and stated in Section 3.2 and Section 4, respectively.
•
6
data model, and represents the abstract entity of welding task information. Management information is to describe information, such as designers, task identifier, approver, etc., which are used to retrieve, modify and reuse welding task. Weld feature information is a key element of the welding task data model, and contains information of joint, weld, and welding requirements. It directly affects process planning of robotic welding. Joint information contains geometry information and groove information which is to describe types and dimension of groove. Weld information is to describe path, types, dimension of weld. Path of weld is used to plan motion path of welding robot. Types and dimension of weld directly affect selection of welding methods and welding parameters. Welding requirements is to describe requirements for welding quality, imperfections and tolerances. Welding quality includes welding quality standard and grades determined by designer. Imperfections include undercut, gas hole, etc., which are used to evaluate welding quality. Due to possible deformation of workpiece caused by welding heat, tolerances requirements include tolerances of linear dimensions, angular dimensions, straightness, etc. Components information includes material information and geometry information of components to be welded together. Geometry of the components can be tube or sheet. Due to different weld ability of materials, materials types of the components directly affect selection of welding methods and welding parameters.
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Fig. 9. EXPRESS-G representation of Joint_type.
family which is used to enable all information of a product to be exchanged among different software [29]. EXPRESS can be used in two forms. One is a formal language that is based on lexical notation and syntax [30]. The other is a graphical form called EXPRESS-G, which provides a compact and rich illustration [31]. Although EXPRESS is not a programming language, but it is a powerful tool to describe types defined by users. Advantages of EXPRESS include object-oriented characteristic, textual schema, and readability. As a language recommended in STEP, EXPRESS is supported widely by various software tools for product design and process planning. Therefore, EXPRESS-G diagram and EXPRESS language are adopted to design the welding task data model for robotic welding in detail. 4.2. Welding task data model Based on requirements analysis in Section 3.1, schema named Weld_task _model is designed by using EXPRESS to fulfill the information requirements, as described in Fig. 4. Entity named WeldTask is the toplevel entity of the schema. As depicted in Fig. 5, three main attributes of WeldTask are Its_management_info, Its_components and Its_weld_features, used to describe management information, component information and welding feature information, respectively. Attributes of entity named Management_info contain Its_id, Its_author, Its_time, etc. Entity named Component is used to describe information of identifier, material and geometry of workpieces to be welded together. Entity named Weld_feature is used to describe joint information, weld information and welding requirement, and designed in detail in Section 4.3.
Fig. 10. EXPRESS representation of Weld_type.
• Geometry information represents topology and dimensions information of components to be welded. Geometry elements such as points, edges, normal, face, etc., are used to describe geometry constraints of weld features.
Based on the structure of the welding task data model, which includes essential information items for process planning and information integration of robotic welding, the welding task data model is designed in detail in Section 4. 4. Detailed design of welding task data model
4.3. Welding feature 4.1. Language for describing model As a key entity of the welding task data model, welding features are used to describe welding requirements, geometry and dimensions of welded area of a workpiece. Entity named Weld_feature is defined as follows by using EXPRESS, as described in Fig. 6. EXPRESS-G
As a powerful and easy-to-use language, EXPRESS is selected to describe complex structure and entities of the welding task data model. EXPRESS is developed in Standard of Exchange Product data (STEP) 7
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Fig. 11. EXPRESS-G representation of Weld_type.
Curve_feature. Their common attributes contain Its_id, Connected_parts, Its_joint, Its_weld, and Welding_requirement. Attribute called Connected_parts is used to specify workpieces to be welded together. Subtypes of entity called Joint_type, which is designed in detail in Section 4.4, include Butt_joint, Edge_joint, Tee_joint, Corner_joint, Lap_joint, etc. Entity named Weld_type, which is designed in detail in Section 4.5, has subtypes named Butt_weld, Edge_weld, Plug_weld, Fillet_weld, etc. Entity named Requirement is designed in detail in Section 4.6. 4.4. Joint Entity named Joint_type is used to describe information of joint type and groove type of welding feature. Joint_type is defined as follows by using EXPRESS, as described in Fig. 8. EXPRESS-G representation of Joint_type is depicted in Fig. 9. Subtypes of Joint_type include Butt_joint, Edge_joint, Tee_joint, Corner_joint, Lap_joint, etc. Attribute named Its_groove is used to describe type and dimension of groove that is classified into V_groove, J_groove, U_groove, I_groove, etc. Geometry information of joint is defined by sharing geometry entities of STEP, e.g. plane, cylindrical surface, B-spline surface, etc. Geometry attributes of Tee_joint contain Wall_surface, Base_surface. Attributes of Lap_joint include Top_surface and Bottom_surface.
Fig. 12. EXPRESS representation of welding requirement.
representation of Weld_feature is depicted in Fig. 7. Weld_feature has four subtypes named Spot_feature, Linear_feature, Circular_feature and
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Fig. 13. EXPRESS-G representation of welding requirement.
Fig. 14. Welding tasks of front door subassembly of an automobile.
As an important attribute of Weld_type, Its_path is used to describe geometry information of weld. Four subtypes of entity named Path include Point, Line, Circle and B_spline_curve, which refer to STEP. Path has two optional attributes called Its_start_point and Its_end_point that are used to describe position of start point and end point, respectively.
4.5. Weld type Entity named Weld_type is used to describe path information and dimension information of weld of welding feature. Weld_type is designed as follows by using EXPRESS, as described in Fig. 10. EXPRESS-G representation of Weld_type is depicted in Fig. 11. Subtypes of Weld_type include Butt_weld, Edge_weld, Plug_weld, Fillet_weld, Spot_weld, etc. Attributes of Spot_weld contain Spot_weld_diameter, Distance_between_welds and Number_of_welds. Optional attributes of Fillet_weld include Throat_thickness, Leg_length, Number_of_WE, etc. Number_of_WE is used to describe number of weld elements in welding feature.
4.6. Welding requirement Entity named Requirement is used to describe requirements for welding quality, imperfections and tolerances of welding feature. Requirement is defined as follows by using EXPRESS, as described in 9
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welding feature 1 is shown in Fig. 17. It is created manually in format of Part 21.
• The geometry information of the welding task is defined in lines • • •
Fig. 15. Geometry of welding task 1.
#15, #16, and #39. The geometry of top_surface and bottom_surface is defined in lines #15 and #16 respectively. The path of weld is defined in line #39. The material information of the welded components, i.e. part 1 and part 4, is defined in lines #17 and #51. The materials of part 1 and part 4 are SPC270C metal sheet with thickness of 0.7 mm. The requirements of the welding task are defined in lines #11, #18, and #132. The welding quality is required to be class B. The maximum allowable dimension of undercut is 0. The maximum allowable quantity of gas holes is 0. The management information about the welding task is defined in line #2. The creator, time and identifier of the welding task are described in line #2.
As the input of intelligent process planning system, the created welding task file can be used in CAPP that includes expert system and intelligent algorithm. An application framework of the welding task file is described in Fig. 18. The welding task file created in CAD is firstly transmitted to CAPP for process planning of robotic welding. After the welding task file is read by CAPP software with an interpreter, the Part 21 file is parsed into an intermediate file that can be understood by machine. Based on the information parsed from the welding task file, appropriate process plan, which includes welding path and welding parameters, is output by CAPP. OLP software can generate and simulate robotic program in accordance with the welding task file and the process plan. Then the robotic program is transmitted to welding robot controller. Finally, the results of welding operation are fed back to CAD, CAPP, and OLP system. A closed-loop iterative optimization of welding process plan can be carried out in the proposed application framework.
Fig. 16. Geometry of welding feature 1.
Fig. 12. EXPRESS-G representation of Requirement is depicted in Fig. 13. Optional attributes of Requirement contain Its_weld_quality, Its_imperfections and Its_tolerances. Weld_quality is used to specify welding quality standard and grade of welding task. Imperfections is used to determine maximum allowable value of imperfections which include under cut, gas hole, shrinkage of weld root, etc. Tolerances is to describe tolerances of dimension and shape of welding feature after welding. Optional attributes of Tolerances include Linear_dimensions, Angular_dimensions and Straightness, which are to specify tolerances of linear dimensions, angular dimensions and straightness, respectively. Definitions of tolerance grades refer to ISO 13920.
5.2. Evaluation (1) Information integrity In terms of information integrity, the proposed welding task data model includes more information items than other welding task data models, as shown in Table 1. Information of geometry, material, requirements and management of welding tasks is defined in the proposed welding task data model. The information is essential for intelligent process planning of robotic welding. Geometry information is used to plan optimal welding path. Information of material and requirements is necessary for selecting appropriate welding method and welding parameters. Management information plays an important role in reuse of welding tasks and iterative optimization of welding process plans.
5. Case study 5.1. Welding tasks of front door subassembly In order to demonstrate the feasibility of the proposed welding task data model, a case study about the front door subassembly of an automobile is implemented. As shown in Fig. 14, the front door subassembly of an automobile consists of four metal sheets, which are part 1, part 2, part 3, and part 4. There are four welding tasks to be completed by welding robots. Welding task 1 is designed to connect part 1 and part 4. The geometry of welding task 1 is shown in Fig. 15. Welding task 1 consists of three welding features, i.e. welding feature 1, welding feature 2, and welding feature 3. P1, P2, P3, and P4 refer to the start point or the end point of welding seams. The geometry of welding feature 1 is shown in Fig. 16. Here, the top_surface and the bottom_surface are two B_spline_surface. The curve_weld_part_4 and the curve_weld_part_1 are two B_spline_curve. The thickness of part 4 is h (h = 0.7 mm). The gap between the top_surface and the bottom_surface is g (g = 2 mm). P1 and P2 are the start point and the end point of welding seam respectively. The task data of welding feature 1 is defined based on the proposed welding task data model. The file format of the task data is an extension of ISO 10303-21 (Part 21) specifications. The welding task file of
(2) Compatibility and effectiveness The proposed welding task data model is implemented in format of Part 21, as shown in Fig. 17. Due to cross-platform characteristics of Part 21 format, information integration among CAD/CAPP/CAM for robotic welding can be realized. The case study shows the correctness of the proposed welding task data model by creating a task file in Part 21 format. Compared to traditional process planning systems that need input additional information described in 2D drawing manually, the efficiency of process planning can be improved by using the proposed welding task data model. As shown in Table 2, qualitative efficiency comparisons of process planning by using different welding task definition methods are analyzed. With respect to time spent in creating welding tasks, it needs more time to generate traditional 2D drawing because of the extra conversion from 3D to 2D. Conversely, welding 10
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Fig. 17. Task data of welding feature 1.
Fig. 18. Application framework of a welding task data file.
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Table 2 Qualitative view of efficiency of process planning systems with different task definition methods. Task definition methods
Time spent in creating welding task
Time spent in transmitting task
Time spent in inputting task into CAPP system
Traditional 2D drawing Proposed welding task data model
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References
tasks can be generated directly from 3D model by using proposed welding task model. Owing to the compatibility of proposed welding task model, the time spent in transmitting welding tasks is less than traditional 2D drawing. Because of the machine-readability of proposed welding task data model, the time spent in inputting welding tasks into CAPP is less than traditional 2D drawing that may require artificial interpretation and manual input.
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6. Conclusions It is obvious that a welding task data model can facilitate the intelligence and efficiency of process planning for robotic welding. However, current welding task data models lack essential information items and compatibility. In the proposed welding task data model, accurate geometry information, material information, requirements information, and management information is defined by using EXPRESS and EXPRESS-G. Due to cross-platform characteristics, Part 21 format is adopted to define welding task data file. In order to demonstrate the correctness of the proposed welding task data model, a case study about the front door subassembly of an automobile is implemented. Welding task data file of a welding feature of the automobile front door subassembly is created. An application framework of welding task data files is presented to show the advantages of welding task data model. The output task file can be used not only for planning process in CAPP but also for programming in OLP. Iterative optimization of welding process plan can be achieved in the proposed application framework. With the support of the proposed welding task data model, information requirements for intelligent process planning of robotic welding can be fulfilled. Due to machine readability and compatibility of the welding task data model, seamless information integration of CAD/CAPP/CAM platforms can be realized. Due to the object-oriented characteristic, the welding task data model can be easily extended and applied to more complex welding tasks. Future work will focus on designing a welding robot controller, which can directly read welding task file, to improve decision-making intelligence of welding robots. Moreover, algorithms of welding feature recognition need to be developed for improving efficiency of defining welding task files. CRediT authorship contribution statement Weidong Shen: Conceptualization, Methodology, Writing - original draft, Software. Tianliang Hu: Conceptualization, Project administration, Writing - review & editing. Chengrui Zhang: Conceptualization, Supervision. Yingxin Ye: Software. Zhengyu Li: Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The work is supported by the National Natural Science Foundation of China (Grant No. 51875323) and Key R&D Program of Shandong Province (Major scientific and technological innovation project) (Grant No. 2019JZZY010123). 12