Applications of Data Mining to Diagnosis and Control of Manufacturing Processes

Abstract

In the majority of manufacturing companies large amounts of data are collected and stored, related to designs, products, equipment, materials, manufacturing processes etc. Utilization of that data for the improvement of product quality and lowering manufacturing costs requires extraction of knowledge from the data, in the form of conclusions, rules, relationships and procedures. Consequently, a rapidly growing interest in DM applications in manufacturing organizations, including the development of complex DM systems, can be observed in recent years (Chen et al. 2004; Chen et al. 2005; Dagli & Lee, 2001; Hur et al., 2006; Malh & Krikler, 2007; Tsang et al., 2007). A comprehensive and insightful characterization of the problems in manufacturing enterprises, as well as the potential benefits from the application of data mining (DM) in this area was presented in (Shahbaz et al., 2006). Examples and general characteristics of problems related to the usage of data mining techniques and systems in a manufacturing environment can be found in several review papers (Harding et al., 2006; Kusiak, 2006; Wang, 2007). Application of DM techniques can bring valuable information, both for designing new processes and for control of currently running ones. Designing the processes and tooling can be assisted by varied computer tools, including simulation software, expert systems based on knowledge acquired from human experts, as well as the knowledge extracted semi automatically by DM methods. The proper choice of the manufacturing process version and its parameters allows to reduce the number of necessary corrections resulting from simulation and/or floor tests. The knowledge obtained by DM methods can significantly contribute to the right decision making and optimum settings of the process parameters. In the design phase two main forms of knowledge may be particularly useful: the decision logic rules in the form: ‘IF (conditions) THEN (decision class)’ and the regression–type relationships. Although the latter have been widely utilized before the emergence of DM methods (e.g. in the form of empirical formulas) and the rules created by the human experts were also in use, the computational intelligence (CI) methods (learning systems) have remarkably enhanced possibilities of the knowledge extraction and its quality. For the manufacturing process control many varied methods are used, ranging from paper Statistical Process Control (SPC) charts to automated closed loop systems. In spite of the