Haitham Rashidy. Knowledge-based quality control in manufacturing processes with application to the automotive industry

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1 Haitham Rashidy Knowledge-based quality control in manufacturing processes with application to the automotive industry Herbert Utz Verlag München

2 Forschungsberichte IWB Band 227 Zugl.: Diss., München, Techn. Univ., 2008 Bibliografische Information der Deutschen Nationalbibliothek: Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über abrufbar. Dieses Werk ist urheberrechtlich geschützt. Die dadurch begründeten Rechte, insbesondere die der Übersetzung, des Nachdrucks, der Entnahme von Abbildungen, der Wiedergabe auf fotomechanischem oder ähnlichem Wege und der Speicherung in Datenverarbeitungsanlagen bleiben auch bei nur auszugsweiser Verwendung vorbehalten. Copyright Herbert Utz Verlag GmbH 2009 ISBN Printed in Germany Herbert Utz Verlag GmbH, München

3 Contents Contents List of abbreviations xi List of symbols xv List of figures xvii List of tables xxi 1 Introduction Quality and knowledge - The big picture Current situation in operative quality control Problem definition Objective and approach Thesis structure 8 2 Literature review Overview Terms and definitions Quality control and fault diagnosis Knowledge-based systems Process monitoring Introduction Statistical process control Fault pattern recognition for SPC Neural networks for process monitoring Model-based diagnostic systems Introduction Model-based diagnosis with qualitative knowledge Knowledge-based diagnostic systems Modeling and diagnosis in body-in-white assembly Integration of diagnosis, decision support and process control Introduction Process control Fault-adaptive control 29 v

4 Contents SPC-based feedback control Run-by-run control Knowledge-based applications for process adjustment Decision support issues Uncertainty Cost of quality (COQ) Human decision-making and error Conclusion 36 3 Field study Overview Description of the investigated production facility General information Facility performance from a quality control perspective Vehicle body development process Design and planning procedures Stamping operations and BIW BIW quality control procedures Fault sources affecting dimensional quality of BIW Door assembly Assembly operations sequence Door quality assessment Conclusion 53 4 Overview of the proposed system Proposed system structure On the rationale of the proposed system structure Assumptions Fault recognition module Overview Process considerations for network design and training Monitoring strategy 59 vi

5 Contents Statistical distribution model of monitored characteristics Error type I and error type II Evaluation criterion Module structure Development of the neural network paradigm Network structure Learning Activation function Training data Stage 1: Univariate stage Step 1: General classifier (NN-11) Training and test procedures Simulation results Results Step 2: Specialized classifiers (NN-121 to NN-125) Stage 2: Multivariate stage Effect of moving recognition window Conclusion 81 6 Fault identification module Overview and module structure Knowledge acquisition Procedure General-purpose tools Case-specific tools Results General diagnostics Case-specific diagnostics Representation of the results Rule base (knowledge representation) Inference engine (knowledge reproduction) 98 vii

6 Contents Procedure Fuzzy inference Conflict resolution Implementation and results Conclusion Decision module Overview and module structure Fault probability criterion Objective of fault probability consideration Bayes Theorem Implementation Recovery cost criterion Objective of recovery cost consideration Theoretical background Prevention-appraisal-failure (PAF) quality cost model Taguchi s quality loss function (QLF) Proposed online quality cost assessment Validation and implementation Conclusion Integration and reuse Overview Experimental setup Prototype of the integrated system Illustrative reuse scenario Impact on production performance An assessment Overview Technical assessment Economic assessment Summary and future research References 141 viii

7 Contents 12 Appendix Companies named in the thesis Some fundamentals of neural networks Some fundamentals of fuzzy math 181 ix

8 1.1 Quality and knowledge - The big picture 1 Introduction 1.1 Quality and knowledge - The big picture Quality as a success factor Quality is a decisive success factor and one of the competitive edges of modern manufacturers. In many production scenarios, maintaining high product quality and production efficiency entails the extensive use of advanced process monitoring, control and adjustment techniques. In this regard, pertaining literature reports that quality related costs may run at 20-40% of sales [JURAN & GYRNA 1988, TAGUCHI et al. 1989]. In recent decades, researchers and international organizations stressed that the cost of quality is not the price of creating a quality product or service. It is the cost of not creating a quality product or service, hence, more intuitively known as the cost of poor quality [BESTERFIELD 1990]. Advanced process design and offline fault analysis methods do reduce failure risks [WHITNEY 1996]. But, offline methods alone are not enough since any process will drift if no control is applied [DEL CASTILLO 2002]. According to ROSS 1995, continuous adjustment, even within tolerance limits, is a must for more competitive products that bear minimized losses to the society. The premise that each failure has a root cause, causes are preventable, and prevention is cheaper [BESTERFIELD 1990] represents the underlying motivation for a number of research activities in the field of online quality control. Such research initiatives addressed process monitoring [ANAGUN 1998, BARGHASH & SANTARISI 2004], fault diagnosis and recovery [BALLÉ & FUESSEL 2000, BEN-GAL et al. 2003] and their integration [DEL CASTILLO 2002, GUH 2003] in order to deal with production disturbances, ranging from minor quality nonconformance to complete equipment failure. In sharp contrast to research activities, a study of manufacturing priorities in the industrial and the consumer goods sectors (Figure 1.1) shows a rather paradoxical situation [A. T. KEARNEY 2005]. Increasing product quality and eliminating defectives are not on the top of the priority list when production costs are considered. The finding is alerting in the light of the impact of product quality on the overall performance and profitability. In spite of the current advances in quality engineering, this key function promises yet a greater profit potential in industrial practices if it is assigned more resources. 1

9 1 Introduction Manufacturing priorities Reduce material costs 100 Reference Increase utilization Increase throughput Eliminate defects Impact on cost Impact on revenue Impact on profitability Figure 1.1: Impact of manufacturing priorities on cost, revenue and profitability [A. T. KEARNEY 2005]. All values are relative to the upper left entry marked as reference. Quality as a shared responsibility Quality, maintenance and operation personnel, often separate teams, cooperate to solve quality problems as quickly and as efficiently as possible. The know-how of the quality planning team complements the task. Such shared responsibilities and extensive experience involved in the fault recovery process have led to the development of computer-aided approaches (CAx) in the three areas to facilitate the interdisciplinary communication and to yield a more efficient production process. The nature of quality problems Generally, if complete failure or equipment stoppage occurs, e. g. due to crash, the fault cause is easy to identify and correct. Most original equipment manufacturers (OEM) have integrated standard diagnosis functions in their control software. Commercial product data management (PDM) systems offer further assistance in the monitoring and diagnosis of production machinery. The situation is different when dealing with quality problems of assembled products. In practice, manufacturers install quality inspection equipment in order to prevent defective products from reaching the customer. However, these systems have limited abilities as to fault identification, diagnosis, and recovery. Inferring a fault root cause or a recovery action based on the analysis 2

10 1.2 Current situation in operative quality control of a product s deviation from target quality characteristics depends heavily on the experience and the know-how of the involved personnel. Knowledge as a success factor Knowledge is regarded as one of the most important issues affecting the success of individuals and organizations. The role of knowledge in the industry has been emphasized in recent years [OETZMANN 2005, RUDOLF 2007], as companies have become more aware of their dependence on qualified staff due to increasing market pressure. Knowledge preserving measures, such as knowledge and competence management policies or the implementation of expert knowledge-based applications, contribute to sustaining and reinforcing the competitiveness of a company [HANNULA et al. 2003]. The most valuable asset in knowledge-related practices is by far the human expert who represents the ultimate decision-making machine. Figure 1.2 shows a simplified view of data processing into knowledge. : processing Decisions Advice Information Data e. g. Human experts e. g. Knowledge-based systems e. g. Management information systems Figure 1.2: Volume versus value in data processing (after [HARRIS-JONES 1995]) 1.2 Current situation in operative quality control DEL CASTILLO 2002 summarizes the difference between quality control and traditional engineering process control (EPC) as given in Table 1.1. He suggests that these two apparently opposing viewpoints need to be reconciled and notes that the need exists for the increased application of EPC-based techniques for quality control. 3

11 1 Introduction Table 1.1: Process control versus quality control [DEL CASTILLO 2002] Process control Quality control Output(s) Process variable(s) Quality characteristic(s) Input(s) Process variable(s) Process variable(s) Control action Automatic Usually manual Considering an arbitrary automated series production process, schematically represented in Figure 1.3, it can be said that the process control comprises two main tasks: data acquisition and control action. Acquisition of process and product data involves sensor technology, measurement principles and monitoring techniques. The control block handles aspects of data interpretation, reference process behavior, decision logic, and feedback of the control action. Automated data acquisition has witnessed relatively more advances in recent decades than the automation of the control action. There are several reasons why automated inline inspection of product specifications has been applied: short reaction time, reduction of rework and scrap, reduction of logistic costs and high measurement capacity, to name a few. The basic disadvantage of inline measurement is the high initial cost. In addition, the accessibility of all needed quality criteria is not always guaranteed. Many applications allow equipment and process parameters to be monitored as well, such that alarms can be automatically signaled when unusual process conditions occur. However, this is highly process specific and is not always possible. For example, it is not feasible to automatically monitor the condition of fixtures in an assembly line. Deducing the control action is more complex. Modern production processes pose challenging fault diagnosis tasks, which may entail costly scrap and lag until the fault is eliminated. Experience plays a significant role in assessing the fault severity; what a young engineer, by nature more conservative, considers as scrap might well be rework for a more experienced specialist. Moreover, in order to maintain a stable process, it is not only important to accumulate experience but to ensure its availability and accessibility also. A parallel factor adding to the difficulty of such diagnostic tasks is the often encountered lack of documentation since most manufacturers rely on short fault description in spreadsheet form. Noteworthy is that recent advances in PDM systems and computerized maintenance management systems (CMMS) have improved the situation. However, contrary to both acronyms, the focus on operative implementation, rather than on management, is still lagging. Specialized CAx tools for troubleshooting product quality problems and fault root cause analysis are a rare commodity in practical applications. 4

12 1.3 Problem definition Production process Process parameters Product quality Control Logic, decision, knowledge, control action, etc. + - Reference Monitoring (Data acquisition) Sensors, Metrology, Monitoring, etc. Figure 1.3. Control of a production process in a closed-loop representation Among the different research directions initiated in response to the unsatisfactory situation was the implementation of model-based quality control techniques in analogy to conventional EPC paradigms [SACHS et al. 1995, DEL CASTILLO 2002, CARLSON & SÖDERBERG 2003]. Also, approaches rooted in the fields of artificial intelligence (AI) and knowledge engineering were used for the same purpose. Models for process stability analysis, fault diagnosis, decision support and corrective actions were successfully built in this way [CHANG & HO 1999, CHEN & HWANG 1992, CAIAZZO et al. 2004]. This thesis belongs to the latter category, and addresses the use of AI and knowledgebased systems (KBS) for quality control in automotive body-in-white production. 1.3 Problem definition Body-in-white (BIW) production is a representative example of a class of complex automated manufacturing processes, where the aforementioned situation is witnessed. Figure 1.4 illustrates the result of a study conducted by CEGLAREK & SHI 1995 showing that maintenance problems dominate the production phase of the automotive body. Of the studied cases, 56% were related to subassemblies, 20% to framing and 2% to final assemblies. The remaining 22% were due to panel variations. The relations between the dimensional variation of the vehicle and its functional performance, as well as assembly line failures during production are not very clearly understood [HU 1997, CEGLAREK & SHI 1997, CARLSON & SÖDERBERG 2003]. As such, the process of fault elimination is highly subjective and vulnerable due to a number of factors that can be summarized as follows: Monitoring techniques, such as statistical process control (SPC), do not explain the root causes of defects [PAN 2002]. The employment of pure engineering judgment brings an element of uncertainty to the decision making process. 5

13 1 Introduction Regular employee rotations affect the level of available experience. Lacking fault documentation yields inefficient knowledge management. The link between planning and operation teams weakens after start of production (SOP). Fault handling is a shared responsibility between maintenance, quality and operation personnel, which adds organizational costs to the fault recovery process. The total losses due to fault diagnosis effort and time are often not fully quantified and the real costs of a fault are underestimated. The process stages are physically similar. It is difficult to predict product specifications since no accurate process models are available. Only end-of-line (EOL) measurements are possible. Monitoring all process parameters affecting the geometry, such as positions of fixtures, is not feasible. BIW production in high-wage countries has developed into a nearly fully automated process with integrated inline quality monitoring solutions for 100% inspection, and, hence, is well suited for the application of online CAx tools. As detailed later in Chapter 3, a field study conducted at a German automotive manufacturer substantiated the necessity of exploiting further improvement potentials in the handling of quality problems (Figure 1.5). 100 Root cause percentages Design Installation Supplier Maintenance 0 Pre-volume Launch Single shift full production Production phase Two shift full production Figure 1.4: BIW dimensional fault root cause classification [CEGLAREK & SHI 1995] 6

14 1.4 Objective and approach Production line Improvement potential Fault analysis and correction Deviation Cycles Quality inspection Figure 1.5: Current fault analysis procedures in BIW represent an improvement potential for the reduction of production costs 1.4 Objective and approach Based on the previous discussion, the objective of this research can be formulated as: The development of a knowledge-based system (KBS) for fault diagnosis and decision support in online quality control of manufacturing processes with the example of body-in-white production The KBS aims at aiding the human analyst with tools for quantitative knowledge representation that can be annexed to existing monitoring systems. The objective can also be seen as an attempt to realize semi-automated closed-loop handling of quality problems. The term knowledge-based is generally defined by Knowledge-based Systems 1 as follows. Knowledge-based systems support human decision-making, learning and action. Such systems are capable of cooperating with human users and so the quality of support given and the manner of its presentation are important issues. Throughout the thesis, the focus will remain on the automotive BIW production, as described in the problem definition. Data obtained from a field study and recommendations from the literature will be used to identify the solution requirements and to design a modular diagnostic system, with a fault knowledge base as its core compo- 1 Knowledge-Based Systems is the international, interdisciplinary and application-oriented journal on KBS. <www.sciencedirect.com/science/journal/ > 7

15 1 Introduction nent. The three shaded blocks in Figure 1.6 represent the three basic tasks that will be investigated in the course of this research, which are: Fault recognition: the detection of abnormalities in the process Fault identification: associating an abnormality with a special cause Decision: applying or deferring a process adjustment The scope of this research does not include the measurement system. Neither will the implementation of the corrective action be addressed in the sense of physical manipulation of the process parameters. 1.5 Thesis structure This chapter presented an introduction to the research problem and the objective of the thesis. Chapter 2 reviews pertaining literature on process monitoring, fault diagnosis and related issues. Previous approaches to integrating fault knowledge databases in online control are also presented. Findings from a field study conducted at an automotive production facility are included in Chapter 3. Chapter 4 gives an overview of the architecture of the proposed diagnostic system. The development of the system components is described in Chapter 5, Chapter 6 and Chapter 7. Chapter 8 illustrates an exemplary application scenario and a software prototype of the integrated system. A technical and economical assessment of the system is given in Chapter 9. A summary and perspectives for further research can be found in Chapter 10. Table 1.2 gives an overview of the thesis. Process Measurement Process interface Adjustment Fault recognition Fault identification Decision Figure 1.6: Basic tasks in the fault recovery loop 8

16 1.5 Thesis structure Table 1.2: Overview of the thesis Chapter Content 1 Introduction, problem definition and objective Review of pertaining literature in order to establish the theoretical need for further KBS research in a quality control context Field study showing the situation in a BIW production facility and establishing the practical need for alternatives in operative quality control Overview of the proposed solution consisting of a modular structure of specialized submodels Details of the fault recognition module responsible for triggering alarm signals in the case of quality deviations Details of the fault identification module responsible for determining the fault root cause for the quality deviation and providing the user with troubleshooting instructions Details of the decision module responsible for issuing a recommendation to the user in case immediate process interruption is required 8 Discussion of the system integration and a software prototype 9 Discussion of the impact of the proposed system on the overall performance of BIW production in technical and economical terms 10 Summary and future research directions 9

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