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Towards a Condition-Monitoring Framework for Quality Assurance in Intelligent Multistage Manufacturing EnvironmentÕ
Proceedigs of the 15th IFAC Symposium on Proceedigs of the 15th IFAC Symposium on Information Control Problems in Manufacturing Proceedigs of of the 15th 15th IFAC Symposium Symposium on Proceedigs the IFAC on Information Control Problems in Manufacturing May 11-13, 2015. Ottawa, Canada Available Information Control Problems in Manufacturing Information Control Problems in Manufacturingonline at www.sciencedirect.com May 11-13, 2015. Ottawa, Canada May May 11-13, 11-13, 2015. 2015. Ottawa, Ottawa, Canada Canada
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IFAC-PapersOnLine 48-3 (2015) 2089–2094
Towards a Condition-Monitoring Towards a Condition-Monitoring Towards a Condition-Monitoring Framework for Quality Assurance in Framework for Quality Assurance in Framework for Quality Assurance in Intelligent Multistage Manufacturing Intelligent Manufacturing Intelligent Multistage Multistage Manufacturing Environment Environment Environment
O.J. Bakker ∗∗ S.M. Ratchev ∗∗ A.A. Popov ∗∗ O.J. Bakker ∗∗ S.M. Ratchev ∗∗ A.A. Popov ∗∗ O.J. O.J. Bakker Bakker S.M. S.M. Ratchev Ratchev A.A. A.A. Popov Popov ∗ ∗ The University of Nottingham, Manufacturing and Process University Nottingham, Manufacturing and Process ∗ ∗ The The of Nottingham, Manufacturing and Process Technologies Researchof Nottingham, NG7 2RD, (e-mail: The University University ofDivision, Nottingham, Manufacturing and UK Process Technologies Research Division, Nottingham, NG7 2RD, UK Technologies Research Division, Division, Nottingham, Nottingham, NG7 NG7 2RD, 2RD, UK UK (e-mail: (e-mail: {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) Technologies Research (e-mail: {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) Abstract: Manufacturing processes, such as machining, welding or tooling and assembly are Abstract: processes, such as welding or tooling and assembly are Abstract: Manufacturing processes, such machining, welding or and are increasinglyManufacturing automated to reduce the costs andmachining, furthermore to negate the decrease of a skilled Abstract: Manufacturing processes, such as as machining, welding or tooling tooling and assembly assembly are increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled labor force. Each process has its own key parameters that are required to be within a certain increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled labor force. Each its own key parameters that to be within aa certain labor force. Each process has its key that are required to tolerance band in process order tohas ensure product quality, such as are e.g. required surface finish. The application labor force. Each process has its own own key parameters parameters that are required to be be within within a certain certain tolerance band in order to ensure product quality, such as e.g. surface finish. The application tolerance band in order order to toallows ensure product quality,tosuch such as e.g. e.g. surface finish. finish. The the application of intelligent automation theproduct manufacturer create an environment where sensory tolerance band in ensure quality, as surface The application of intelligent automation allows the manufacturer to create an environment where the sensory of intelligent allows the create the systems that automation are inherently connected to intelligentto components are utilized where for manufacturing of intelligent automation allows the manufacturer manufacturer tocomponents create an an environment environment where the sensory sensory systems that are inherently connected to intelligent are utilized for manufacturing systems that are inherently connected to intelligent components are utilized for manufacturing process monitoring purposes. In addition this framework is meant to be used in a multistage systems that are inherently connected to intelligent components are utilized for manufacturing process monitoring purposes. addition framework meant be process monitoring purposes.toIn Incontrol addition this frameworkofis isvariations meant to to introduced be used used in in aaaatmultistage multistage manufacturing environment thethis propagation upstream process monitoring purposes. In addition this framework is meant to be used in multistage manufacturing environment to control the propagation of variations introduced at manufacturing environment to smart controlsensors the propagation propagation of variations introduced introduced at upstream upstream stations. The widely available in today’s of manufacturing industry can assist to manufacturing environment to control the variations at upstream stations. widely smart sensors in today’s manufacturing can assist to stations. The widely available smart sensors in today’s manufacturing industry can assist to reduce theThe number of available direct dimensional measurement tools required for industry this purpose. stations. The widely available smart sensors in today’s manufacturing industry can assist to reduce the number of direct dimensional measurement tools required for this purpose. reduce the number of direct dimensional measurement tools required for this purpose. In this paper, a generic framework for the design of the monitoring and decision making system reduce the number of direct dimensional measurement tools required for this purpose. In this for design of and making In this paper, paper, a a generic generic framework for the the design of the the monitoring monitoring and decision decision making system is demonstrated with a framework simulated case study in milling, using the tracking of the force system rate as In this paper, a generic framework for the design of the monitoring and decision making system is demonstrated with a simulated case study in milling, using the tracking of the force rate as is demonstrated with a simulated case study in milling, using the tracking of the force rate as a successful technique tosimulated detect certain events, such as tool breakage. Moreover, the data can is demonstrated with a case study in milling, using the tracking of the force rate as a successful technique to detect certain events, such as tool breakage. Moreover, the data can a successful technique to detect certain events, such as tool breakage. Moreover, the data can be used for compensation in the next production or assembly stage for variation reduction in a successful technique to detect certain events, such as tool breakage. Moreover, the data can be used for compensation in the next production or assembly stage for variation reduction in be used for compensation in the next production or assembly stage for variation reduction multistage manufacturing processes. be used formanufacturing compensation processes. in the next production or assembly stage for variation reduction in in multistage multistage manufacturing manufacturing processes. processes. multistage © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Process monitoring, Manufacturing systems, Automation, Product quality, Keywords: Manufacturing systems, Automation, Product quality, Keywords: Process Process monitoring, Manufacturing systems, Automation, Product quality,Signal Multivariate qualitymonitoring, control, Production systems, Decision making, Data processing, Keywords: Process monitoring, Manufacturing systems, Automation, Product quality, Multivariate quality control, Production systems, Decision making, Data processing, Multivariate quality quality control, control, Production Production systems, systems, Decision Decision making, making, Data Data processing, processing, Signal Signal processing Multivariate Signal processing processing processing 1. INTRODUCTION In reality this means that commercially available monitor1. In this means that commercially available monitor1. INTRODUCTION INTRODUCTION In reality reality thistools means thaton commercially availableprocesses, monitoring software focus commonly applied 1. INTRODUCTION In reality this means that commercially available monitoring software tools focus on commonly applied processes, ing software tools focus on commonly applied processes, whereas niche processes, such as e.g. linear friction weldManufacturing process monitoring attracted a consider- ing software tools focus on commonly applied processes, whereas niche processes, such as e.g. linear friction weldManufacturing process monitoring attracted a considerwhereas niche processes, such as e.g. linear friction welding, and the manufacturing of special products are still Manufacturing process monitoring monitoring attracted considerable amount of attention over the years. The reason for this whereas niche processes, suchofasspecial e.g. linear friction weldManufacturing process attracted aa considering, and the manufacturing products are still able amount of attention over the years. The reason for this ing, and the manufacturing of special products are still relatively uncovered as it requires a tailor-made frameable amount of attention over the years. The reason for this is that when machining is done within the right tolerances, and the manufacturing of special products are still able amount of attentionisover the years.the The reason for this ing, relatively uncovered as it requires a tailor-made frameis that when machining done within right tolerances, relatively uncovered as it requires a tailor-made framework which in many cases is commercially not attractive. is that when machining is done within the right tolerances, the required quality of the produced part is achieved and relatively uncovered as it requires a tailor-made frameis that when machining is done within the right tolerances, work which in many cases is commercially not attractive. the required quality is and work which in is not developments of manufacturing towards the required quality of ofofthe the produced part is achieved achieved and However, hence the monitoring the produced machiningpart process contributes work which in many many cases cases is commercially commerciallysystems not attractive. attractive. the required quality of the produced part is achieved and However, developments of manufacturing systems towards hence the monitoring of the machining process contributes However, developments of manufacturing systems integrated, intelligent and therefore information-rich syshence thetomonitoring monitoring of the the machining machining process contributes greatlythe the manufacturing qualityprocess assurance. Many However, developments of manufacturing systems towards towards hence of contributes integrated, intelligent and therefore information-rich sysgreatly to the manufacturing quality assurance. Many integrated, intelligent and therefore information-rich systems opens up new avenues for process and tool monitoring greatly to the manufacturing quality assurance. Many research projects in this area claimed reasonable success intelligent and therefore information-rich sysgreatly to the manufacturing quality reasonable assurance.success Many integrated, tems opens up new avenues for process and tool monitoring research projects in this area claimed tems opens up new avenues for process and tool monitoring applications in what are considered as niche applications. research projects in this area claimed reasonable success in laboratory and real industrial conditions. However, tems opens up new avenues for process and tool monitoring research projects in this area claimed reasonable success applications in what are considered as niche applications. in laboratory and However, in are as niche in laboratory implementation and real real industrial industrial conditions. However, applications industry-wide of theconditions. process monitoring applications in what what are considered considered niche applications. applications. in laboratory and real industrial conditions. However, Abellan-Nebot and Subir´ on (2010)asidentify six generic industry-wide implementation of the process monitoring Abellan-Nebot and Subir´ o n (2010) identify six industry-wide implementation ofal.,the the process monitoring techniques is limited (Liang et of 2004). Thismonitoring is due to steps industry-wide implementation process Abellan-Nebot and Subir´ on n (2010) (2010) identify six generic generic which haveand to be addressed for identify the development of techniques is limited (Liang et al., 2004). This is due to Abellan-Nebot Subir´ o six generic steps which have to be addressed for the development of techniques is limited (Liang et al., 2004). This is due to (Liang et al., 2004): techniques is 2004): limited (Liang et al., 2004). This is due to intelligent steps which have to be addressed for the development of process monitoring applications: (Liang et al., steps which have to be addressed for the development of intelligent process monitoring applications: (Liang et al., 2004): (Liang et al., 2004): intelligent process monitoring applications: • The lack of robust sensor hardware and monitoring intelligent process monitoring applications: (1) “The different sensor systems applied to monitor •• The lack (1) “The different sensor systems applied to monitor The lack of of robust robust sensor sensor hardware hardware and and monitoring monitoring algorithms. • The lack of robust sensor hardware and monitoring (1) different sensor machining processes, algorithms. (1) “The “The different sensor systems systems applied applied to to monitor monitor machining processes, algorithms. • algorithms. The lack of concerted effort in the research commumachining processes, (2) the most effective signal processing techniques” to •• The lack of concerted effort in the research commumachining processes, (2) most effective processing The lack of of concerted concerted effort effort in in the the research research commucommunity. lack • The (2) the the most effective signal processing techniques” techniques” to to extract some specificsignal features, nity. (2) the most effective signal processing techniques” to extract some specific features, • nity. The lack of standardization in automation. extract some specific features, (3) “the most frequent sensory features applied in mod•• nity. The lack of standardization in automation. extract some specific features, (3) “the frequent sensory features applied in modThe lack standardization in automation. automation. of consistent success. • lack of of consistent standardization in (3) most frequent sensory eling most machining processes, •• The The lack success. (3) “the “the most frequent sensory features features applied applied in in modmodeling machining processes, The lack of consistent success. • The lack of consistent success. eling machining processes, (4) the sensory feature selection and extraction methods eling machining processes, The authors wish to acknowledge the support of the European (4) sensory feature selection and (4) the the sensory featuresensory selection and extraction extraction methods methods The authors wish to acknowledge the support of the European for using relevant information, (4) the sensory feature selection and extraction methods Commission through 7th Framework call FP7for using relevant sensory information, The authors authors wish the to acknowledge acknowledge theProgramme support of ofunder the European European The wish to the support the for using relevant sensory information, (5) the designrelevant of experiments required to model a maCommission through the 7th Framework Programme under call FP7for using sensory information, AAT-2007-RTD-1 (FLEXA; grant agreement 213734) andcall support Commission the Programme under FP7(5) the design of experiments model aof maCommission through through the 7th 7th Framework Framework Programme under call FP7(5) design of required to model chining operation with the required minimumto exAAT-2007-RTD-1 (FLEXA; grantProject agreement 213734) and support (5) the the design of experiments experiments required toamount model a aof mamaof EPSRC through SAMULET 5: Processing Advanced AAT-2007-RTD-1 (FLEXA; grant agreement 213734) and support chining operation with the minimum amount exAAT-2007-RTD-1 (FLEXA; grant agreement 213734) and support of EPSRC through SAMULET Project 5: Processing Advanced chining operation with the minimum amount of perimental data, chining operation with the minimum amount of exexMaterials; through Evolvable Assembly - Toward Open, of through SAMULET Project Processing Advanced perimental data, of EPSRC EPSRCand through SAMULET Project 5: 5:Systems Processing Advanced Materials; and through Evolvable Assembly Systems Toward Open, perimental data, data, Adaptable andthrough Context-Aware Equipment Systems. Materials; Evolvable Assembly Systems -- Toward perimental Materials; and and through Evolvable Assembly and Systems Toward Open, Open,
Adaptable and Context-Aware Equipment and Systems. Adaptable Adaptable and and Context-Aware Context-Aware Equipment Equipment and and Systems. Systems. Copyright © 2015 IFAC 2163Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Copyright © 2015 IFAC 2163 Copyright © 2015 IFAC 2163 Peer review under responsibility of International Federation of Automatic Copyright © 2015 IFAC 2163Control. 10.1016/j.ifacol.2015.06.397
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(6) the main characteristics of several artificial intelligence techniques to facilitate their application / selection.” Furthermore, Abellan-Nebot and Subir´ on (2010) identify the main modeling techniques available to capture certain features from the measured signal, as the application of multiple regression, neural networks, or the use of support vector machines. The main feature selection/extraction techniques are: forward elimination, forward/backward elimination, variable ranking, orthogonal arrays and principle component analysis. Additionally, the required building of artificial intelligence-based process models are discussed. These process models can be used as reference or to predict future values of the process parameters. Additionally, the techniques can be used to optimize the manufacturing process (Abellan-Nebot and Subir´ on, 2010; Chandrasekaran et al., 2010). It should be noted here, that wavelet analysis, empirical mode decomposition and other types of spectral analyses are also used to find certain features in the signals, such as tool breakage (Liang et al., 2004). Wavelet analysis is in some cases superior to more traditional approaches relying on filters and Fourier transforms. Wavelets can be used to de-noise the measured signal and find abrupt changes associated with tool wear and breakage Zhu et al. (2009). Furthermore, Shin et al. (2006) discuss the relation between cloud manufacturing and lower level manufacturing execution systems and lower levels of control, and additionally how to connect the manufacturing process monitoring system with framework architecture. The data which has been captured and processed into useful information can also be shared between the several stages of a manufacturing cell or line. This opens up the way for feed-forward compensatory control for the reduction of production variation (Shi and Zhou, 2009). It should be noted here from the literature review by Shi and Zhou (2009) that the calculated feed-forward control action relies on the direct in-line dimensional measurement of the manufactured part or assembly. However, dimensional accuracy can also be established indirectly by means of machining process monitoring (Liang et al., 2004). The main observation from this brief review of related work in manufacturing process monitoring is that process monitoring involves many areas. Furthermore, that manufacturing process monitoring systems are often applied without further integration within the high-level control environment of the factory. As for the current intelligent automation trends in which data-rich manufacturing environments are standard, it can be assumed that the data from measurements of the process variables will typically be available. The aim of this paper is to propose a monitoring framework that serves a dual purpose using the data available in an intelligent manufacturing environment. Firstly, the monitoring framework serves as a quality assurance tool for a single stage in the manufacturing process. Secondly, the data can be further processed to be used to control the variation propagation in multistage manufacturing processes span by the manufacturing cell or line as a whole applying process condition-monitoring techniques
to assist direct in-line dimensional measurement of the part or assembly geometry, and to be able to reduce the number of costly, additional measurement tools. Therefore, in Sec. 2 the available process variables for monitoring purposes are investigated. Secondly, it is discussed what the requirements for the processing software are, followed by a more in-depth discussion of de-noising and filtering, data collection and feature extraction. After which the building and application of the process-knowledge model is treated. Lastly, in Sec. 2.8, an example of the established monitoring framework is given. The results are presented and discussed in Sec. 3. Finally, the key findings are given in Sec. 4. 2. METHOD 2.1 Interfacing The monitoring system should be able to interface with the higher control system of the intelligent manufacturing environment: firstly, to simply store the data to be compliant with the agreed form of quality assurance; secondly, to interface with the several stages within the cell or line, to control the variation propagation in the whole line. 2.2 Process Variable Type Selection A typical data capture system will be required to handle two types of data: (1) Data from the cells sensor systems to optimize the process within the cell. (2) Data for use in traceability and quality systems to guarantee product integrity. These are two different types of data. The first type of data is the data used for the high-level control systems. Some typical examples of these data are: product ID, routing (information provided by ERP/SAP software), Production Order, part orientation, CNC Part Program, machine tool settings (spindle speed, selected tool, programmed tool path, cutting fluid, feed), overstock material (in mm), fixture (geometry and structure). These are all very important for the quality control. This work considers the second type of data, process variables, where the quality of the manufacturing process is assessed using the data that is captured during the machining process. In previous and ongoing research, typical process data for a advanced intelligent manufacturing cell were listed (Liang et al., 2004; Seyrek, 2009; AbellanNebot and Subir´on, 2010). It was investigated how they are supposed to be monitored regarding the sensor selection and usage, and how they can be used for model-based process design, and how they relate to the process parameters for the higher level control systems. A number of these measurable process variables is given in the paragraph below. Available Process Variables Examples of typical measurable process variables in an automated flexible manufacturing cell that determine final product quality are: cutting forces, toolpaths, achieved feed-rates, spindle speed, electrical current in the command signals, robot end-effector path, acoustic emissions, welding wire feed, and so on.
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R • Matlab , for performing matrix vector calculations and has many additional toolboxes for signal processing, wavelets, filtering, artificial neural networks (NN), and so on, • Dedicated software.
2.3 Requirement for Data Processing Software The process variables are typically tracked in time and their behavior is studied similarly to studies of curves in mathematics, typically one analyses its first and second derivatives. The first derivative is used to determine what the gradient of a curve is, where it becomes zero and whether this is a local maximum or minimum in the curve. The second derivative test reveals where the inflection point of the curve is and whether, in case the gradient of the curve becomes zero, these points are local extrema or stationary points. Furthermore, borrowing from the performance tracking and comparison techniques as commonly applied in motorsports, when a displacement is monitored other variables can be tracked against the displacement, for instance, welding process variables against the weld path. This makes it easier to compare two welds. Thirdly, the data processing tool should be able to work with an acceptance corridor and be able to apply the acceptance criteria. In this way the decision making system for quality assurance is based on the limit application. Deviant parts are rejected or placed in a different queue for further inspection. It is impossible for all processes to base these acceptance corridors on theoretical values. In these cases, it might be necessary to establish empirical values. Hence, fourthly, when necessary, the data processing software should be able to establish the upper and lower bounds from an experimental data set. As measurements are taken in a real, not ideal world, there will be noise on the signal. This leads to a final requirement when the monitoring system is only used as a quality assurance tool for the single current stage: the software should be able to deal with electric spikes and to smooth the noise on a signal. When the data of the monitoring system is utilized for the reduction of variation in multistage manufacturing processes, the system should be able to perform the necessary calculations to feed the required information into the next stage to compensate for any deviations introduced at upstream stations. This data can be gathered by ‘simple’ in-line dimensional measurement or by a prediction of the dimensions based on the measurements of other process variables. For this an accurate process-knowledge model is required, which can be used to predict the process outcome, regardless of whether it is built based on statistics, artificial intelligence or a mechanistic/physical model. 2.4 Process Data Collection After it has been decided which aspect(s) of the process should be monitored for diagnosis or prediction purposes and how these are going to be related to the variables that can be measured using the process-knowledge model, the variables should be read from the data-acquisition system into software that can be used for process monitoring. Depending on the processing requirements this can be done in general purpose software such as: R • Microsoft Excel for performing calculations in a spread-sheet,
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In order to prevent beginning-and-end-effects from occurring during further processing and accidentally missing features of interest, as much of the data as reasonably possible should be loaded to the processing program. 2.5 Filtering or De-noising The combination of noise, vibrations and oscillations occurring during a process can be traced in the measured variables. In cases when the trend is analyzed and the signal is differentiated with respect to time, these phenomena can obscure the trend of the key variable during the process. For this reason, some filtering of the signal is essential. However, filtering action introduces extra distortion to the signal. To smooth the signal, often a low-pass filter is used. This means that the signal components in the high frequency range are filtered out. A Fourier transform shows that a steep change in the gradient of the measured signal contains many high frequency components. Hence filtering with a typical low-pass smoothing filter will always make changes with the gradient becoming less steep. This means that one has to pay the price of a less accurate signal when applying filtering action to smooth the signal. When the smoothing filter is designed with a too low cut-off frequency, it will filter out much of the undesired noise etc., but will significantly decrease the accuracy of the signal as the changes in the gradient become duller. Therefore, filter design for signal smoothing filters comprises an optimization of the trade-off between the loss of accuracy in the gradients and the remaining noise content. Within the scope of this work, this means that one has to allow for a small remnant of oscillatory motion in the curve. Wavelet analysis is the next step from the ShortTime Fourier Transform (STFT), which has only fixedtime windows, it is a windowing technique with variablesized regions. A signal will be decomposed into wavelets of different scales and positions (Matsubara and Ibaraki, 2009). For this reason, wavelet analysis enables the analyst to study local phenomena in larger signals and is often used for condition-monitoring purposes, see e.g. Raithatha et al. (2007). For the same reason, wavelet analysis can often de-noise a signal without significant distortion. The better performance of the wavelet de-noising as a means of smoothing the signal can also be seen when the residuals are studied, as for the wavelet de-noising these are marginally larger than the filtered residuals, while trying to optimally preserve the gradient of the oscillatory motion in the residuals. Hence the signal is much better preserved by wavelet de-noising than by filtering. 2.6 Feature Extraction The features are key part in the signal that give information about the process. As was observed in Sec. 1, machine condition-monitoring is the subject of numerous studies. Phenomena like tool breakage or tool wear can be found by changes in the machining forces or through acoustic emission. These changes will have to be found in large
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arrays of data. Other information regarding the surface roughness of certain key characteristics of a product can be found within smaller amounts of data, as often they occur during a key stage in the process, so typically they are extracted in the time domain. Normally, these features can be found by: • Checking command signals. • For certain processes and machines, the machine distinguishes between different phases. • Extra sensors are switched on or off, or reach a certain threshold value. • The tracked variable shows distinctive behavior which can be identified, e.g. with one of the techniques mentioned in Sec. 1 or a wavelet analysis. 2.7 Process Knowledge Model Building a Process Knowledge Model Now that the data is pre-processed and appropriate features are extracted, the data can be processed further for the processknowledge model. From the review of related work in Sec. 1, it can be concluded that there are generally 4 approaches that are used to establish a process-knowledge model: (1) Model-based, ideally every process is modeled from first principles. This offers the best understanding of the process and allows for optimization. Obviously, mathematical process models need rigorous experimental validation as to verify whether the assumptions and linearizations applied to build the model are valid. However, due to the complexity of manufacturing processes and their stochastic nature it proves not always possible to establish a model-based processknowledge model. (2) Experimentally obtained process models form the logic extension of model-based process-knowledge model building. In case the model is too crude to reliably diagnose or predict the process (outcome), experiments can be utilized to establish a reliable process model or process window. (3) Design of Experiment (DoE) allows the engineer or scientist to build a model based on a set of experiments where in each individual experiments some of the settings are changed. Note that if there are more changeable parameters the number of required experiments increases exponentially. Careful planning is required to minimize the number of experiments. (4) Artificial Intelligence can be used to identify a certain underlying model. In a very similar matter, these techniques are also applied for model identification of dynamic processes in the area of advanced control engineering. Depending on the model used to analyse the manufacturing process, its study is mostly done in the time domain, frequency domain, or wavelet domain.
Fig. 1. Lumped parameter representation of thin-walled part-fixturing system. used to be compared against the process-knowledge model to monitor tool wear and or tool breakage, machine health, whether the process remains in the allowable process window, surface roughness, and dimensional accuracy. Additionally, the per stage prediction of the process-knowledge model plays a key role when using the data captured by the monitoring to calculate required control action to compensate in the next stage(s) for the variation build up upstream when the line or cell is considered as a whole, i.e. reducing the variation in multistage manufacturing processes, which will be the next stage of the research. 2.8 Numerical Case Study The authors ongoing research effort in manufacturing process monitoring has been implemented by Bakker et al. (2015) in a linear friction welding process monitoring system. The case study presented here forms a logical extension to that work. It consists of a lumped parameter model which reduces the number of degrees of freedom to seven discrete masses. As it is tuned to model the first dominant modes of vibration of the thin-walled part used in the experimental validation of active fixture design carried out in the paper by Papastathis et al. (2014), it is very similar in transient response. This model is shown in Fig. 1. Here, one can see that the lumped parameter describing the behavior of a thin-plate model consists of 7 rigid masses. The interaction between each of the masses goes through the spring and damper element with stiffness k2 and damping c2 . Each mass has a displacement xi for i = 1, 2, . . . , 7, a machine force Fm can be applied on each of the discrete masses. The machine process used for the verification by Papastathis et al. (2014) is milling and accordingly, the cutting force Fm can reasonably be approximated by: Fm = a [1 + sin (2πnt)] .
(1)
Where a is a scale factor ranging between 5 and 15, see Table 1; t time; and n the spindle number of revolutions. In the simulation, the machining force is applied to masses m1 to m7 , assuming the same length of the workpiece as in Papastathis et al. (2014) and a conveniently short machining time (of only 3 seconds) for the simulation: the feed-rate equates to 42.9 mm/s.
It should be noted here that other approaches are available to study the manufacturing process such as the empirical mode decomposition. Application of the Process Knowledge Model Measured data during current manufacturing processes can then be 2166
Table 1. Numerical Values of constants and parameters. c1 k1 c2 k2
10 Ns/m 1 × 107 N/m 1 Ns/m 8 × 105 N/m
m n a
9.559 × 10−3 kg 24000 rpm 5, 7.5, 10, 12.5, 15 [-]
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All the numerical values for the constants and lumped parameter values are given in Table 1. The fixture is modeled with spring stiffness k1 and damping c1 . Before any machining takes places, L1 is moved to a new position, such that the clamping force, i.e. the reaction force on both locators, becomes 30 N. To found a set of first-order ordinary differential equations (ODEs) that describe the mechanical model of the partfixture system and machining forces Fm that can be solved to study the system’s transient response, firstly the equations of motion of the lumped parameter system are established. Secondly, these second-order ODEs are rewritten into a first-order state-space formulation. The motion of L1 to create the clamping force comes here in the input vector. The machining forces Fm acts as a moving load over masses 2-6, in the same manner as done in Bakker et al. (2011). The studied outputs in this work are the reaction forces on L1 and L2. After solving the equations in first-order state-space formulation R in Matlab , some ‘noise’ has been added using the rand command. Borrowing from the example in Seyrek (2009), six cases are studied, monitoring the system at new, 25% tool life, 50% tool life, 75% tool life, 100% tool life and 100% tool life+tool breakage. Assuming the cutting tool slowly gets blunt (for our purposes here this is assumed to develop linearly) and breaks eventually, the amplitude factor a in Eq. (1) is 5 when the tool is new and increases linearly according to the values shown in Table 1. The tool breakage event is typically characterized by zero force, as the tool will disconnect from the part when it breaks, or when a part of it disintegrates. This event occurs at t = 2.5 s.
Fig. 3. Force rate tracked against time The forces are filtered, differentiated and filtered again to find the first derivative of the trend in the forces. This is shown in Fig. 3. As the cutting forces increase due to the cutting tool getting blunt (Seyrek, 2009), additional statistical modeling can be used to predict when tool change is required. The statistical analyses also assists in achieving dimensional accuracy at this manufacturing stage. The intelligent software framework controlling the multistage manufacturing cell or line can calculate the required compensation action in the next stage utilizing data from the current stage. For this reason, the accuracy of the process-knowledge model of each stage, discussed in Sec. 2.7 is of pivotal importance. The tool breakage can be observed in Fig. 2, as the reaction forces are increased, but even more clearly in Fig. 3 where a large peak can be observed. This makes the tracking of the derivative of the forces (the force rate) in combination with acceptance corridors a very useful tool to locate sudden changes like tool breakage.
3. RESULTS AND DISCUSSION
Fig. 2. Reaction forces on locators L1 and L2 plotted against time. In Fig. 2 the simulated reaction forces on the locators are given. The left-hand side shows the reaction forces at locator L1 and the right-hand side the reaction forces at locator L2. The black lines form the acceptance corridor.
Fig. 4. Spectral analysis of noise and oscillation in reaction forces.
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The noise and force oscillations can also be studied from the filtered residuals, e.g. the peak in the oscillations caused by the tool breakage shows more clearly than in Fig. 2. The space/time frequency behavior can be studied using e.g. wavelets, STFT, or the Hilbert-Huang Transform. In Fig. 4, the power spectral density plot is given. In this plot, a large peak is located at 400 Hz, the input frequency from the cutting forces. The tool breakage excites the mode-shape belonging to the first eigenfrequency, the peak showing at 735 Hz. This is another demonstration that a spectral analysis can be helpful to detect the occurrence of a certain event (Liang et al., 2004) – e.g. tool breakage, chatter, or excessive vibrations due to an improperly fixtured part in its workholder – when data of several production runs can be compared. 4. CONCLUSIONS This work studies the development of a generic monitoring and decision making system for use in the quality assurance of manufacturing processes with the aim to apply it in information-rich, intelligent production environments. It is demonstrated with a simulated case study of milling thin-walled components. It is assumed that the begin and end times of the machining process are extracted from the command signals of the machine tool and the reaction forces at the fixture are captured. The force signals are filtered and further processed. Using the first derivative to monitor the forces, proves sufficient to detect events where there is a sudden change in force such as tool breakage. Additionally, it is shown that differentiating the filtered signal in combination with the application of an acceptance corridor is an adequate form of capturing events with a disruptive nature, such as tool breakage. In an intelligent manufacturing environment, using statistical analyses, the data captured and analyzed at this production stage can also be fed into the next stage to insert extra compensatory control action if required, extending the single-stage monitoring for quality assurance into a multistage reduction of variation. This comes in assistance to the ‘standard’ in-line dimensional accuracy measurement already happening at a set number of stations required to control variation in a multistage manufacturing process.
optimization: a literature review. International Journal of Advanced Manufacturing Technology, 46(5-8), 445– 464. Liang, S.Y., Hecker, R.L., and Landers, R.G. (2004). Machining process monitoring and control: The stateof-the-art. Transactions of the ASME Journal of Manufacturing Science and Engineering, 126(2), 297– 310. Matsubara, A. and Ibaraki, S. (2009). Monitoring and control of cutting forces in machining processes: A review. International Journal of Automation Technology, 3(4), 445–456. Papastathis, T.N., Bakker, O.J., Ratchev, S.M., and Popov, A.A. (2014). Experimental validation of active fixture design methodology. In Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference, 1–10. ASME, Detroit. Raithatha, A., Duncan, S., Jackson, K., and Allwood, J. (2007). Second order cone programming in modeling incremental deformation. In Proceedings of the 2007 American Control Conference, 4841–4846. IEEE, New York. Seyrek, A. (2009). Microsoft Powerpoint Nordmann presentation 02.04.2009.ppt. URL: http://www.nordmann.eu/usa/pdf/ Nordmann-Praesentation.pdf. Accessed on 13 Mar 2015. Shi, J. and Zhou, S. (2009). Quality control and improvement for multistage systems: A survey. IIE Transactions, 41(9), 744–753. Shin, B.C., Kim, G.H., Choi, J.H., Jeon, B.C., Lee, H., Cho, M.W., Han, J.Y., and Park, D.S. (2006). A web-based machining process monitoring system for emanufacturing implementation. Journal of Zhejiang University Science A, 7(9), 1467–1473. Zhu, K., Wong, Y.S., and Hong, G.K. (2009). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools & Manufacture, 49(7-8), 537–553.
REFERENCES Abellan-Nebot, J.V. and Subir´ on, F.R. (2010). A review of machining monitoring systems based on artificial intelligence process models. International Journal of Advanced Manufacturing Technology, 47(1-4), 237–257. Bakker, O.J., Gibson, C., Wilson, P., Lohse, N., and Popov, A.A. (2015). Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition. Mechanical Systems and Signal Processing. (accepted for publication). Bakker, O.J., Popov, A.A., Salvi, E., Merlo, A., and Ratchev, S.M. (2011). Model-based control of an active fixture for advanced aerospace components. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(1), 35–51. Chandrasekaran, M., Muralidhar, M., Murali Krishna, C., and Dixit, U.S. (2010). Application of soft computing techniques in machining performance prediction and 2168
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Towards a Condition-Monitoring Towards a Condition-Monitoring Towards a Condition-Monitoring Framework for Quality Assurance in Framework for Quality Assurance in Framework for Quality Assurance in Intelligent Multistage Manufacturing Intelligent Manufacturing Intelligent Multistage Multistage Manufacturing Environment Environment Environment
O.J. Bakker ∗∗ S.M. Ratchev ∗∗ A.A. Popov ∗∗ O.J. Bakker ∗∗ S.M. Ratchev ∗∗ A.A. Popov ∗∗ O.J. O.J. Bakker Bakker S.M. S.M. Ratchev Ratchev A.A. A.A. Popov Popov ∗ ∗ The University of Nottingham, Manufacturing and Process University Nottingham, Manufacturing and Process ∗ ∗ The The of Nottingham, Manufacturing and Process Technologies Researchof Nottingham, NG7 2RD, (e-mail: The University University ofDivision, Nottingham, Manufacturing and UK Process Technologies Research Division, Nottingham, NG7 2RD, UK Technologies Research Division, Division, Nottingham, Nottingham, NG7 NG7 2RD, 2RD, UK UK (e-mail: (e-mail: {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) Technologies Research (e-mail: {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) {ottojan.bakker,svetan.ratchev,atanas.popov}@nottingham.ac.uk) Abstract: Manufacturing processes, such as machining, welding or tooling and assembly are Abstract: processes, such as welding or tooling and assembly are Abstract: Manufacturing processes, such machining, welding or and are increasinglyManufacturing automated to reduce the costs andmachining, furthermore to negate the decrease of a skilled Abstract: Manufacturing processes, such as as machining, welding or tooling tooling and assembly assembly are increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled labor force. Each process has its own key parameters that are required to be within a certain increasingly automated to reduce the costs and furthermore to negate the decrease of a skilled labor force. Each its own key parameters that to be within aa certain labor force. Each process has its key that are required to tolerance band in process order tohas ensure product quality, such as are e.g. required surface finish. The application labor force. Each process has its own own key parameters parameters that are required to be be within within a certain certain tolerance band in order to ensure product quality, such as e.g. surface finish. The application tolerance band in order order to toallows ensure product quality,tosuch such as e.g. e.g. surface finish. finish. The the application of intelligent automation theproduct manufacturer create an environment where sensory tolerance band in ensure quality, as surface The application of intelligent automation allows the manufacturer to create an environment where the sensory of intelligent allows the create the systems that automation are inherently connected to intelligentto components are utilized where for manufacturing of intelligent automation allows the manufacturer manufacturer tocomponents create an an environment environment where the sensory sensory systems that are inherently connected to intelligent are utilized for manufacturing systems that are inherently connected to intelligent components are utilized for manufacturing process monitoring purposes. In addition this framework is meant to be used in a multistage systems that are inherently connected to intelligent components are utilized for manufacturing process monitoring purposes. addition framework meant be process monitoring purposes.toIn Incontrol addition this frameworkofis isvariations meant to to introduced be used used in in aaaatmultistage multistage manufacturing environment thethis propagation upstream process monitoring purposes. In addition this framework is meant to be used in multistage manufacturing environment to control the propagation of variations introduced at manufacturing environment to smart controlsensors the propagation propagation of variations introduced introduced at upstream upstream stations. The widely available in today’s of manufacturing industry can assist to manufacturing environment to control the variations at upstream stations. widely smart sensors in today’s manufacturing can assist to stations. The widely available smart sensors in today’s manufacturing industry can assist to reduce theThe number of available direct dimensional measurement tools required for industry this purpose. stations. The widely available smart sensors in today’s manufacturing industry can assist to reduce the number of direct dimensional measurement tools required for this purpose. reduce the number of direct dimensional measurement tools required for this purpose. In this paper, a generic framework for the design of the monitoring and decision making system reduce the number of direct dimensional measurement tools required for this purpose. In this for design of and making In this paper, paper, a a generic generic framework for the the design of the the monitoring monitoring and decision decision making system is demonstrated with a framework simulated case study in milling, using the tracking of the force system rate as In this paper, a generic framework for the design of the monitoring and decision making system is demonstrated with a simulated case study in milling, using the tracking of the force rate as is demonstrated with a simulated case study in milling, using the tracking of the force rate as a successful technique tosimulated detect certain events, such as tool breakage. Moreover, the data can is demonstrated with a case study in milling, using the tracking of the force rate as a successful technique to detect certain events, such as tool breakage. Moreover, the data can a successful technique to detect certain events, such as tool breakage. Moreover, the data can be used for compensation in the next production or assembly stage for variation reduction in a successful technique to detect certain events, such as tool breakage. Moreover, the data can be used for compensation in the next production or assembly stage for variation reduction in be used for compensation in the next production or assembly stage for variation reduction multistage manufacturing processes. be used formanufacturing compensation processes. in the next production or assembly stage for variation reduction in in multistage multistage manufacturing manufacturing processes. processes. multistage © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Process monitoring, Manufacturing systems, Automation, Product quality, Keywords: Manufacturing systems, Automation, Product quality, Keywords: Process Process monitoring, Manufacturing systems, Automation, Product quality,Signal Multivariate qualitymonitoring, control, Production systems, Decision making, Data processing, Keywords: Process monitoring, Manufacturing systems, Automation, Product quality, Multivariate quality control, Production systems, Decision making, Data processing, Multivariate quality quality control, control, Production Production systems, systems, Decision Decision making, making, Data Data processing, processing, Signal Signal processing Multivariate Signal processing processing processing 1. INTRODUCTION In reality this means that commercially available monitor1. In this means that commercially available monitor1. INTRODUCTION INTRODUCTION In reality reality thistools means thaton commercially availableprocesses, monitoring software focus commonly applied 1. INTRODUCTION In reality this means that commercially available monitoring software tools focus on commonly applied processes, ing software tools focus on commonly applied processes, whereas niche processes, such as e.g. linear friction weldManufacturing process monitoring attracted a consider- ing software tools focus on commonly applied processes, whereas niche processes, such as e.g. linear friction weldManufacturing process monitoring attracted a considerwhereas niche processes, such as e.g. linear friction welding, and the manufacturing of special products are still Manufacturing process monitoring monitoring attracted considerable amount of attention over the years. The reason for this whereas niche processes, suchofasspecial e.g. linear friction weldManufacturing process attracted aa considering, and the manufacturing products are still able amount of attention over the years. The reason for this ing, and the manufacturing of special products are still relatively uncovered as it requires a tailor-made frameable amount of attention over the years. The reason for this is that when machining is done within the right tolerances, and the manufacturing of special products are still able amount of attentionisover the years.the The reason for this ing, relatively uncovered as it requires a tailor-made frameis that when machining done within right tolerances, relatively uncovered as it requires a tailor-made framework which in many cases is commercially not attractive. is that when machining is done within the right tolerances, the required quality of the produced part is achieved and relatively uncovered as it requires a tailor-made frameis that when machining is done within the right tolerances, work which in many cases is commercially not attractive. the required quality is and work which in is not developments of manufacturing towards the required quality of ofofthe the produced part is achieved achieved and However, hence the monitoring the produced machiningpart process contributes work which in many many cases cases is commercially commerciallysystems not attractive. attractive. the required quality of the produced part is achieved and However, developments of manufacturing systems towards hence the monitoring of the machining process contributes However, developments of manufacturing systems integrated, intelligent and therefore information-rich syshence thetomonitoring monitoring of the the machining machining process contributes greatlythe the manufacturing qualityprocess assurance. Many However, developments of manufacturing systems towards towards hence of contributes integrated, intelligent and therefore information-rich sysgreatly to the manufacturing quality assurance. Many integrated, intelligent and therefore information-rich systems opens up new avenues for process and tool monitoring greatly to the manufacturing quality assurance. Many research projects in this area claimed reasonable success intelligent and therefore information-rich sysgreatly to the manufacturing quality reasonable assurance.success Many integrated, tems opens up new avenues for process and tool monitoring research projects in this area claimed tems opens up new avenues for process and tool monitoring applications in what are considered as niche applications. research projects in this area claimed reasonable success in laboratory and real industrial conditions. However, tems opens up new avenues for process and tool monitoring research projects in this area claimed reasonable success applications in what are considered as niche applications. in laboratory and However, in are as niche in laboratory implementation and real real industrial industrial conditions. However, applications industry-wide of theconditions. process monitoring applications in what what are considered considered niche applications. applications. in laboratory and real industrial conditions. However, Abellan-Nebot and Subir´ on (2010)asidentify six generic industry-wide implementation of the process monitoring Abellan-Nebot and Subir´ o n (2010) identify six industry-wide implementation ofal.,the the process monitoring techniques is limited (Liang et of 2004). Thismonitoring is due to steps industry-wide implementation process Abellan-Nebot and Subir´ on n (2010) (2010) identify six generic generic which haveand to be addressed for identify the development of techniques is limited (Liang et al., 2004). This is due to Abellan-Nebot Subir´ o six generic steps which have to be addressed for the development of techniques is limited (Liang et al., 2004). This is due to (Liang et al., 2004): techniques is 2004): limited (Liang et al., 2004). This is due to intelligent steps which have to be addressed for the development of process monitoring applications: (Liang et al., steps which have to be addressed for the development of intelligent process monitoring applications: (Liang et al., 2004): (Liang et al., 2004): intelligent process monitoring applications: • The lack of robust sensor hardware and monitoring intelligent process monitoring applications: (1) “The different sensor systems applied to monitor •• The lack (1) “The different sensor systems applied to monitor The lack of of robust robust sensor sensor hardware hardware and and monitoring monitoring algorithms. • The lack of robust sensor hardware and monitoring (1) different sensor machining processes, algorithms. (1) “The “The different sensor systems systems applied applied to to monitor monitor machining processes, algorithms. • algorithms. The lack of concerted effort in the research commumachining processes, (2) the most effective signal processing techniques” to •• The lack of concerted effort in the research commumachining processes, (2) most effective processing The lack of of concerted concerted effort effort in in the the research research commucommunity. lack • The (2) the the most effective signal processing techniques” techniques” to to extract some specificsignal features, nity. (2) the most effective signal processing techniques” to extract some specific features, • nity. The lack of standardization in automation. extract some specific features, (3) “the most frequent sensory features applied in mod•• nity. The lack of standardization in automation. extract some specific features, (3) “the frequent sensory features applied in modThe lack standardization in automation. automation. of consistent success. • lack of of consistent standardization in (3) most frequent sensory eling most machining processes, •• The The lack success. (3) “the “the most frequent sensory features features applied applied in in modmodeling machining processes, The lack of consistent success. • The lack of consistent success. eling machining processes, (4) the sensory feature selection and extraction methods eling machining processes, The authors wish to acknowledge the support of the European (4) sensory feature selection and (4) the the sensory featuresensory selection and extraction extraction methods methods The authors wish to acknowledge the support of the European for using relevant information, (4) the sensory feature selection and extraction methods Commission through 7th Framework call FP7for using relevant sensory information, The authors authors wish the to acknowledge acknowledge theProgramme support of ofunder the European European The wish to the support the for using relevant sensory information, (5) the designrelevant of experiments required to model a maCommission through the 7th Framework Programme under call FP7for using sensory information, AAT-2007-RTD-1 (FLEXA; grant agreement 213734) andcall support Commission the Programme under FP7(5) the design of experiments model aof maCommission through through the 7th 7th Framework Framework Programme under call FP7(5) design of required to model chining operation with the required minimumto exAAT-2007-RTD-1 (FLEXA; grantProject agreement 213734) and support (5) the the design of experiments experiments required toamount model a aof mamaof EPSRC through SAMULET 5: Processing Advanced AAT-2007-RTD-1 (FLEXA; grant agreement 213734) and support chining operation with the minimum amount exAAT-2007-RTD-1 (FLEXA; grant agreement 213734) and support of EPSRC through SAMULET Project 5: Processing Advanced chining operation with the minimum amount of perimental data, chining operation with the minimum amount of exexMaterials; through Evolvable Assembly - Toward Open, of through SAMULET Project Processing Advanced perimental data, of EPSRC EPSRCand through SAMULET Project 5: 5:Systems Processing Advanced Materials; and through Evolvable Assembly Systems Toward Open, perimental data, data, Adaptable andthrough Context-Aware Equipment Systems. Materials; Evolvable Assembly Systems -- Toward perimental Materials; and and through Evolvable Assembly and Systems Toward Open, Open,
Adaptable and Context-Aware Equipment and Systems. Adaptable Adaptable and and Context-Aware Context-Aware Equipment Equipment and and Systems. Systems. Copyright © 2015 IFAC 2163Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Copyright © 2015 IFAC 2163 Copyright © 2015 IFAC 2163 Peer review under responsibility of International Federation of Automatic Copyright © 2015 IFAC 2163Control. 10.1016/j.ifacol.2015.06.397
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(6) the main characteristics of several artificial intelligence techniques to facilitate their application / selection.” Furthermore, Abellan-Nebot and Subir´ on (2010) identify the main modeling techniques available to capture certain features from the measured signal, as the application of multiple regression, neural networks, or the use of support vector machines. The main feature selection/extraction techniques are: forward elimination, forward/backward elimination, variable ranking, orthogonal arrays and principle component analysis. Additionally, the required building of artificial intelligence-based process models are discussed. These process models can be used as reference or to predict future values of the process parameters. Additionally, the techniques can be used to optimize the manufacturing process (Abellan-Nebot and Subir´ on, 2010; Chandrasekaran et al., 2010). It should be noted here, that wavelet analysis, empirical mode decomposition and other types of spectral analyses are also used to find certain features in the signals, such as tool breakage (Liang et al., 2004). Wavelet analysis is in some cases superior to more traditional approaches relying on filters and Fourier transforms. Wavelets can be used to de-noise the measured signal and find abrupt changes associated with tool wear and breakage Zhu et al. (2009). Furthermore, Shin et al. (2006) discuss the relation between cloud manufacturing and lower level manufacturing execution systems and lower levels of control, and additionally how to connect the manufacturing process monitoring system with framework architecture. The data which has been captured and processed into useful information can also be shared between the several stages of a manufacturing cell or line. This opens up the way for feed-forward compensatory control for the reduction of production variation (Shi and Zhou, 2009). It should be noted here from the literature review by Shi and Zhou (2009) that the calculated feed-forward control action relies on the direct in-line dimensional measurement of the manufactured part or assembly. However, dimensional accuracy can also be established indirectly by means of machining process monitoring (Liang et al., 2004). The main observation from this brief review of related work in manufacturing process monitoring is that process monitoring involves many areas. Furthermore, that manufacturing process monitoring systems are often applied without further integration within the high-level control environment of the factory. As for the current intelligent automation trends in which data-rich manufacturing environments are standard, it can be assumed that the data from measurements of the process variables will typically be available. The aim of this paper is to propose a monitoring framework that serves a dual purpose using the data available in an intelligent manufacturing environment. Firstly, the monitoring framework serves as a quality assurance tool for a single stage in the manufacturing process. Secondly, the data can be further processed to be used to control the variation propagation in multistage manufacturing processes span by the manufacturing cell or line as a whole applying process condition-monitoring techniques
to assist direct in-line dimensional measurement of the part or assembly geometry, and to be able to reduce the number of costly, additional measurement tools. Therefore, in Sec. 2 the available process variables for monitoring purposes are investigated. Secondly, it is discussed what the requirements for the processing software are, followed by a more in-depth discussion of de-noising and filtering, data collection and feature extraction. After which the building and application of the process-knowledge model is treated. Lastly, in Sec. 2.8, an example of the established monitoring framework is given. The results are presented and discussed in Sec. 3. Finally, the key findings are given in Sec. 4. 2. METHOD 2.1 Interfacing The monitoring system should be able to interface with the higher control system of the intelligent manufacturing environment: firstly, to simply store the data to be compliant with the agreed form of quality assurance; secondly, to interface with the several stages within the cell or line, to control the variation propagation in the whole line. 2.2 Process Variable Type Selection A typical data capture system will be required to handle two types of data: (1) Data from the cells sensor systems to optimize the process within the cell. (2) Data for use in traceability and quality systems to guarantee product integrity. These are two different types of data. The first type of data is the data used for the high-level control systems. Some typical examples of these data are: product ID, routing (information provided by ERP/SAP software), Production Order, part orientation, CNC Part Program, machine tool settings (spindle speed, selected tool, programmed tool path, cutting fluid, feed), overstock material (in mm), fixture (geometry and structure). These are all very important for the quality control. This work considers the second type of data, process variables, where the quality of the manufacturing process is assessed using the data that is captured during the machining process. In previous and ongoing research, typical process data for a advanced intelligent manufacturing cell were listed (Liang et al., 2004; Seyrek, 2009; AbellanNebot and Subir´on, 2010). It was investigated how they are supposed to be monitored regarding the sensor selection and usage, and how they can be used for model-based process design, and how they relate to the process parameters for the higher level control systems. A number of these measurable process variables is given in the paragraph below. Available Process Variables Examples of typical measurable process variables in an automated flexible manufacturing cell that determine final product quality are: cutting forces, toolpaths, achieved feed-rates, spindle speed, electrical current in the command signals, robot end-effector path, acoustic emissions, welding wire feed, and so on.
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R • Matlab , for performing matrix vector calculations and has many additional toolboxes for signal processing, wavelets, filtering, artificial neural networks (NN), and so on, • Dedicated software.
2.3 Requirement for Data Processing Software The process variables are typically tracked in time and their behavior is studied similarly to studies of curves in mathematics, typically one analyses its first and second derivatives. The first derivative is used to determine what the gradient of a curve is, where it becomes zero and whether this is a local maximum or minimum in the curve. The second derivative test reveals where the inflection point of the curve is and whether, in case the gradient of the curve becomes zero, these points are local extrema or stationary points. Furthermore, borrowing from the performance tracking and comparison techniques as commonly applied in motorsports, when a displacement is monitored other variables can be tracked against the displacement, for instance, welding process variables against the weld path. This makes it easier to compare two welds. Thirdly, the data processing tool should be able to work with an acceptance corridor and be able to apply the acceptance criteria. In this way the decision making system for quality assurance is based on the limit application. Deviant parts are rejected or placed in a different queue for further inspection. It is impossible for all processes to base these acceptance corridors on theoretical values. In these cases, it might be necessary to establish empirical values. Hence, fourthly, when necessary, the data processing software should be able to establish the upper and lower bounds from an experimental data set. As measurements are taken in a real, not ideal world, there will be noise on the signal. This leads to a final requirement when the monitoring system is only used as a quality assurance tool for the single current stage: the software should be able to deal with electric spikes and to smooth the noise on a signal. When the data of the monitoring system is utilized for the reduction of variation in multistage manufacturing processes, the system should be able to perform the necessary calculations to feed the required information into the next stage to compensate for any deviations introduced at upstream stations. This data can be gathered by ‘simple’ in-line dimensional measurement or by a prediction of the dimensions based on the measurements of other process variables. For this an accurate process-knowledge model is required, which can be used to predict the process outcome, regardless of whether it is built based on statistics, artificial intelligence or a mechanistic/physical model. 2.4 Process Data Collection After it has been decided which aspect(s) of the process should be monitored for diagnosis or prediction purposes and how these are going to be related to the variables that can be measured using the process-knowledge model, the variables should be read from the data-acquisition system into software that can be used for process monitoring. Depending on the processing requirements this can be done in general purpose software such as: R • Microsoft Excel for performing calculations in a spread-sheet,
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In order to prevent beginning-and-end-effects from occurring during further processing and accidentally missing features of interest, as much of the data as reasonably possible should be loaded to the processing program. 2.5 Filtering or De-noising The combination of noise, vibrations and oscillations occurring during a process can be traced in the measured variables. In cases when the trend is analyzed and the signal is differentiated with respect to time, these phenomena can obscure the trend of the key variable during the process. For this reason, some filtering of the signal is essential. However, filtering action introduces extra distortion to the signal. To smooth the signal, often a low-pass filter is used. This means that the signal components in the high frequency range are filtered out. A Fourier transform shows that a steep change in the gradient of the measured signal contains many high frequency components. Hence filtering with a typical low-pass smoothing filter will always make changes with the gradient becoming less steep. This means that one has to pay the price of a less accurate signal when applying filtering action to smooth the signal. When the smoothing filter is designed with a too low cut-off frequency, it will filter out much of the undesired noise etc., but will significantly decrease the accuracy of the signal as the changes in the gradient become duller. Therefore, filter design for signal smoothing filters comprises an optimization of the trade-off between the loss of accuracy in the gradients and the remaining noise content. Within the scope of this work, this means that one has to allow for a small remnant of oscillatory motion in the curve. Wavelet analysis is the next step from the ShortTime Fourier Transform (STFT), which has only fixedtime windows, it is a windowing technique with variablesized regions. A signal will be decomposed into wavelets of different scales and positions (Matsubara and Ibaraki, 2009). For this reason, wavelet analysis enables the analyst to study local phenomena in larger signals and is often used for condition-monitoring purposes, see e.g. Raithatha et al. (2007). For the same reason, wavelet analysis can often de-noise a signal without significant distortion. The better performance of the wavelet de-noising as a means of smoothing the signal can also be seen when the residuals are studied, as for the wavelet de-noising these are marginally larger than the filtered residuals, while trying to optimally preserve the gradient of the oscillatory motion in the residuals. Hence the signal is much better preserved by wavelet de-noising than by filtering. 2.6 Feature Extraction The features are key part in the signal that give information about the process. As was observed in Sec. 1, machine condition-monitoring is the subject of numerous studies. Phenomena like tool breakage or tool wear can be found by changes in the machining forces or through acoustic emission. These changes will have to be found in large
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arrays of data. Other information regarding the surface roughness of certain key characteristics of a product can be found within smaller amounts of data, as often they occur during a key stage in the process, so typically they are extracted in the time domain. Normally, these features can be found by: • Checking command signals. • For certain processes and machines, the machine distinguishes between different phases. • Extra sensors are switched on or off, or reach a certain threshold value. • The tracked variable shows distinctive behavior which can be identified, e.g. with one of the techniques mentioned in Sec. 1 or a wavelet analysis. 2.7 Process Knowledge Model Building a Process Knowledge Model Now that the data is pre-processed and appropriate features are extracted, the data can be processed further for the processknowledge model. From the review of related work in Sec. 1, it can be concluded that there are generally 4 approaches that are used to establish a process-knowledge model: (1) Model-based, ideally every process is modeled from first principles. This offers the best understanding of the process and allows for optimization. Obviously, mathematical process models need rigorous experimental validation as to verify whether the assumptions and linearizations applied to build the model are valid. However, due to the complexity of manufacturing processes and their stochastic nature it proves not always possible to establish a model-based processknowledge model. (2) Experimentally obtained process models form the logic extension of model-based process-knowledge model building. In case the model is too crude to reliably diagnose or predict the process (outcome), experiments can be utilized to establish a reliable process model or process window. (3) Design of Experiment (DoE) allows the engineer or scientist to build a model based on a set of experiments where in each individual experiments some of the settings are changed. Note that if there are more changeable parameters the number of required experiments increases exponentially. Careful planning is required to minimize the number of experiments. (4) Artificial Intelligence can be used to identify a certain underlying model. In a very similar matter, these techniques are also applied for model identification of dynamic processes in the area of advanced control engineering. Depending on the model used to analyse the manufacturing process, its study is mostly done in the time domain, frequency domain, or wavelet domain.
Fig. 1. Lumped parameter representation of thin-walled part-fixturing system. used to be compared against the process-knowledge model to monitor tool wear and or tool breakage, machine health, whether the process remains in the allowable process window, surface roughness, and dimensional accuracy. Additionally, the per stage prediction of the process-knowledge model plays a key role when using the data captured by the monitoring to calculate required control action to compensate in the next stage(s) for the variation build up upstream when the line or cell is considered as a whole, i.e. reducing the variation in multistage manufacturing processes, which will be the next stage of the research. 2.8 Numerical Case Study The authors ongoing research effort in manufacturing process monitoring has been implemented by Bakker et al. (2015) in a linear friction welding process monitoring system. The case study presented here forms a logical extension to that work. It consists of a lumped parameter model which reduces the number of degrees of freedom to seven discrete masses. As it is tuned to model the first dominant modes of vibration of the thin-walled part used in the experimental validation of active fixture design carried out in the paper by Papastathis et al. (2014), it is very similar in transient response. This model is shown in Fig. 1. Here, one can see that the lumped parameter describing the behavior of a thin-plate model consists of 7 rigid masses. The interaction between each of the masses goes through the spring and damper element with stiffness k2 and damping c2 . Each mass has a displacement xi for i = 1, 2, . . . , 7, a machine force Fm can be applied on each of the discrete masses. The machine process used for the verification by Papastathis et al. (2014) is milling and accordingly, the cutting force Fm can reasonably be approximated by: Fm = a [1 + sin (2πnt)] .
(1)
Where a is a scale factor ranging between 5 and 15, see Table 1; t time; and n the spindle number of revolutions. In the simulation, the machining force is applied to masses m1 to m7 , assuming the same length of the workpiece as in Papastathis et al. (2014) and a conveniently short machining time (of only 3 seconds) for the simulation: the feed-rate equates to 42.9 mm/s.
It should be noted here that other approaches are available to study the manufacturing process such as the empirical mode decomposition. Application of the Process Knowledge Model Measured data during current manufacturing processes can then be 2166
Table 1. Numerical Values of constants and parameters. c1 k1 c2 k2
10 Ns/m 1 × 107 N/m 1 Ns/m 8 × 105 N/m
m n a
9.559 × 10−3 kg 24000 rpm 5, 7.5, 10, 12.5, 15 [-]
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All the numerical values for the constants and lumped parameter values are given in Table 1. The fixture is modeled with spring stiffness k1 and damping c1 . Before any machining takes places, L1 is moved to a new position, such that the clamping force, i.e. the reaction force on both locators, becomes 30 N. To found a set of first-order ordinary differential equations (ODEs) that describe the mechanical model of the partfixture system and machining forces Fm that can be solved to study the system’s transient response, firstly the equations of motion of the lumped parameter system are established. Secondly, these second-order ODEs are rewritten into a first-order state-space formulation. The motion of L1 to create the clamping force comes here in the input vector. The machining forces Fm acts as a moving load over masses 2-6, in the same manner as done in Bakker et al. (2011). The studied outputs in this work are the reaction forces on L1 and L2. After solving the equations in first-order state-space formulation R in Matlab , some ‘noise’ has been added using the rand command. Borrowing from the example in Seyrek (2009), six cases are studied, monitoring the system at new, 25% tool life, 50% tool life, 75% tool life, 100% tool life and 100% tool life+tool breakage. Assuming the cutting tool slowly gets blunt (for our purposes here this is assumed to develop linearly) and breaks eventually, the amplitude factor a in Eq. (1) is 5 when the tool is new and increases linearly according to the values shown in Table 1. The tool breakage event is typically characterized by zero force, as the tool will disconnect from the part when it breaks, or when a part of it disintegrates. This event occurs at t = 2.5 s.
Fig. 3. Force rate tracked against time The forces are filtered, differentiated and filtered again to find the first derivative of the trend in the forces. This is shown in Fig. 3. As the cutting forces increase due to the cutting tool getting blunt (Seyrek, 2009), additional statistical modeling can be used to predict when tool change is required. The statistical analyses also assists in achieving dimensional accuracy at this manufacturing stage. The intelligent software framework controlling the multistage manufacturing cell or line can calculate the required compensation action in the next stage utilizing data from the current stage. For this reason, the accuracy of the process-knowledge model of each stage, discussed in Sec. 2.7 is of pivotal importance. The tool breakage can be observed in Fig. 2, as the reaction forces are increased, but even more clearly in Fig. 3 where a large peak can be observed. This makes the tracking of the derivative of the forces (the force rate) in combination with acceptance corridors a very useful tool to locate sudden changes like tool breakage.
3. RESULTS AND DISCUSSION
Fig. 2. Reaction forces on locators L1 and L2 plotted against time. In Fig. 2 the simulated reaction forces on the locators are given. The left-hand side shows the reaction forces at locator L1 and the right-hand side the reaction forces at locator L2. The black lines form the acceptance corridor.
Fig. 4. Spectral analysis of noise and oscillation in reaction forces.
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The noise and force oscillations can also be studied from the filtered residuals, e.g. the peak in the oscillations caused by the tool breakage shows more clearly than in Fig. 2. The space/time frequency behavior can be studied using e.g. wavelets, STFT, or the Hilbert-Huang Transform. In Fig. 4, the power spectral density plot is given. In this plot, a large peak is located at 400 Hz, the input frequency from the cutting forces. The tool breakage excites the mode-shape belonging to the first eigenfrequency, the peak showing at 735 Hz. This is another demonstration that a spectral analysis can be helpful to detect the occurrence of a certain event (Liang et al., 2004) – e.g. tool breakage, chatter, or excessive vibrations due to an improperly fixtured part in its workholder – when data of several production runs can be compared. 4. CONCLUSIONS This work studies the development of a generic monitoring and decision making system for use in the quality assurance of manufacturing processes with the aim to apply it in information-rich, intelligent production environments. It is demonstrated with a simulated case study of milling thin-walled components. It is assumed that the begin and end times of the machining process are extracted from the command signals of the machine tool and the reaction forces at the fixture are captured. The force signals are filtered and further processed. Using the first derivative to monitor the forces, proves sufficient to detect events where there is a sudden change in force such as tool breakage. Additionally, it is shown that differentiating the filtered signal in combination with the application of an acceptance corridor is an adequate form of capturing events with a disruptive nature, such as tool breakage. In an intelligent manufacturing environment, using statistical analyses, the data captured and analyzed at this production stage can also be fed into the next stage to insert extra compensatory control action if required, extending the single-stage monitoring for quality assurance into a multistage reduction of variation. This comes in assistance to the ‘standard’ in-line dimensional accuracy measurement already happening at a set number of stations required to control variation in a multistage manufacturing process.
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