Application of Machine Learning and Expert Systems to Statistical Process Control (spc) Chart Intefqbwtatiion

Statistical Process Control (SPC) Charts are one of several tools used in Quality Control. Other tools include flow charts, histograms, cause-and-effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process, signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division (ALDIJTI) has developed a hybrid machine-learninglexpert-system prototype which automates the process of constructing and interpreting control charts. INTRODUCTION The Air Force Logistics Command (AFLC) has provided TQM and Quality Control training to its employees for several years now. In particular, Statistical Process Control has been emphasized in this effort. While many data collection efforts have been undertaken within AFLC, the SPC Quality Control tool has been under-utilized due to the lack of experienced personnel to identify and interpret patterns within the control charts. The AISC has developed a prototype software tool which draws control charts, identifies various chart patterns, advises what each pattern means, and suggests possible corrective actions. The application is easily modifiable for process specific applications through simple modifications to the knowledge base portion using any word processing software. The remainder of this paper consists of the following sections : (1) CONTROL CHARTS (2) SOFTWARE FUNCTIONALITY (3) SOFTWARE DESIGN (4) MACHINE LEARNING (5) EXPERT SYSTEM (6) CONCLUSION Section (1) provides a more in-depth explanation of the purpose of control charts. Section (2) details the initial functional requirements for the SPC software, and section (3) outlines the design approach used to implement the system requirements. Sections (4) and (5) examine in detail the roles of machine learning and expert system techniques respectively. Finally, section (6) offers some basic conclusions resulting from this effort. Two attachments are included after the references. ATTACHMENT A provides a list of the chart patterns of interest and their methods of identification. ATTAC B enumerates and explains the twenty statistical features used by the machine learning tool. CONTROL CHARTS An example of a control chart is given below in FIGURE 1. A pun chart is a plot of a process measurement (e.g. bore diameter or time to process an insurance claim for example) on the vertical axis (y-axis) against time on the horizontal axis (x-axis). A control chart is simply a run chart with statistically determined upper (Upper Control Limit UCL) and lower (Lower Control Limit LCL) lines drawn on either side of the process average. These limits are calculated by running a process untouched, taking samples of the process measurement, and applying the appropriate statistical formulas (references [3-91). The random fluctuation of points within the limits results from variation built into the process. Such random variation is natural, results from common causes within the system (e.g. design, choice of machine, preventative maintenance, etc.), and can only be affected by changing the system itself. However, points which fall outside of the control limits or which form "unnatural" patterns indicate that some of the variation within the process may be due assignable causes. Assignable causes of variation (e.g. measurement errors, unplanned events, freak occurrences, etc.) can be identified and result from occurrences that are not part of the process. The purpose of drawing the control chart is to detect any unusual causes of variation in the process, signalled by abnormal points or patterns on the graph. The AISC developed software tool automatically identifies nine types of patterns which indicate the presence of assignable causes of variation in a process. Examples of these patterns are given in FIGURES 2 10. Each such pattern is associated with generic advice about what may be happening at that point in the process. More detailed information about each of the nine patterns is given in ATTACHMENT A. ........................................................................................................................................... Y-axis : Process --"-----"-----"---------------------------*---------------------------v ;.s.k :> Measurement , . . .? I? . , , . . , . . . . 5 , . . . . 2 . P. s . , . . . : ': , * . . . . . . . .Q..... . : I .G.,.. j .+ .I G., .-%.a,*' ... . . . . . . . . I a , , .. : '.,-d : .b .... "0 S UCL