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Toward Automated Intelligent Manufacturing Systems (AIMS)

School of Electrical and Computer Engineering, Purdue University, Electrical Engineering Building, 465 Northwestern Avenue, West Lafayette, Indiana 47907-2035, USA
School of Electrical and Computer Engineering, Purdue University, Electrical Engineering Building, 465 Northwestern Avenue, West Lafayette, Indiana 47907-2035, USA
Krannert School of Management, Purdue University, 403 West State Street, West Lafayette, Indiana 47907-2056, USA
Krannert School of Management, Purdue University, 403 West State Street, West Lafayette, Indiana 47907-2056, USA
hoiming{at}alumni.virginia.edu
ersoy{at}purdue.edu
herbm{at}purdue.edu
kemal{at}purdue.edu

Information technology (IT) has been the driver of increased productivity in the manufacturing and service sectors, bringing real-time information to decision makers and process owners to improve process behavior and performance. Thus, organizations have invested heavily in training their employees to use IT in a disciplined, scientific way to make process improvements. This has spawned such popular initiatives as Six Sigma, yielding significant returns, but at considerable investment in training in statistical-analysis and decision-making tools. Can aspects of the decision-making process be automated, letting humans do what they do best (create, define, and measure) and machines (e.g., learning machines) do what they do best (analyze)? We propose an automated intelligent manufacturing system (AIMS) for analysis and decision making that mines real-time or historical data, and uses statistical and computational-intelligence algorithms to model and optimize enterprise processes. The algorithms employed involve a regression support vector machine (SVM) for model construction and a genetic algorithm (GA) for model optimization. Performance of AIMS was compared to Six-Sigma-trained teams employing statistical methodologies, such as design of experiments (DOE), to improve a simulated manufacturing operation, a three-stage TV-manufacturing process, where the objectives were to maximize yield, minimize cycle time and its variation, and minimize manufacturing costs, which were affected by conflicting defects and their causes. AIMS generally outperformed the teams on the above criteria, required relatively little data and time to train the SVM, and was easy to use. AIMS could serve as a productivity springboard for enterprises in existing and emergent technologies, such as nanotechnology and biotechnology/life sciences, where environment and miniaturization may make human monitoring and intervention difficult or infeasible.

Key words: support vector machine; genetic algorithm; Six Sigma; design of experiments (DOE); response-surface designs; machine learning; computational intelligence; intelligent systems
History: received April 2004; revised October 2005; accepted December 2005.