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In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model. †

An automated system relies mostly on a robot, rather than a human operator. In the automated system considered in this paper, a human operator mainly verifies the product quality, where the performance of the human is affected by his or her characteristics. To present this kind of system, an ABM is better than DES to simulate the role of the human operator. This is because the human characteristics are dynamic and are affected significantly by time and environment. This paper presents a DES-ABM model which simulates the performance of a human operator in a human-machine cooperative environment. It may enable this model to be utilized for further development in controller toward the supervisory control.