Qualitative model-based diagnosis driven by visual inspection defect data

The motivation for this work is the desire to maximize the use of visual inspection defect data by moving from quality insurance to quality assurance through active feedback into a process control system. The objective of such machine vision integration is to minimize the response time from defect detection to fault correction. The authors have previously published papers discussing the overall requirements and elements of such a closed loop system. This paper concentrates on identifying methods for establishing the causes of the visible product defects in the manufacturing process. These methods will form the diagnostic element of the overall system. The inspection data provide the symptoms which initiate and direct the diagnostic process. A qualitative model of the manufacturing process is used to generate the diagnostic hypotheses. A systems approach is required to discriminate among these competing plausible hypotheses. The approach adopted tries to relate the physical nature of how the defects in the product are actually formed with the physical manufacturing process. The various manufacturing machines are modeled in terms of their structure, behavior and function. The aim is to implicitly include the causal relationships between the visible physical defects and their causes in the model. Such relationships are normally developed based on experience in maintaining and troubleshooting the manufacturing process. Such experiential-based approaches suffer all the problems associated with expert systems for large scale complex manufacturing plants. Knowledge representation is crucial for the success of this approach. This approach has been developed for discrete event manufacturing processes and attribute defect inspection data.