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Search for more accurate system reliability and failure prediction in auto industry

July 22nd, 2014

A team of researchers from Wayne State University recently received a $350,000 award from the National Science Foundation for the project, "Failure Prediction and Reliability Analysis of Ultra-High Strength Steel Autobody Manufacturing Systems by Utilizing Material Microstructure Properties."

The research is the first attempt to incorporate material microstructure and micro-damage information into a reliability study that fundamentally improves the accuracy of failure and reliability prediction. The automotive industry has been facing challenges in failure prediction and reliability analysis of manufacturing tool systems, so this methodology is a potential answer as it can be applied to the auto body manufacturing system of ultra-high strength steels.

"Our research team will develop statistical methods to analyze and extract the microstructure statistical characteristics and features of workpiece – a piece of metal or other material that is in the process of being worked on or has actually been cut or shaped by a tool or machine – and tool materials that determine the strength of different kinds of micro particles and tool damage process," said Qingyu Yang, Ph.D., assistant professor of industrial and systems engineering in Wayne State's College of Engineering. "In addition, we will develop a physical-statistical model with the incorporation of the extracted microstructural features to describe the tool degradation process. Based on the developed degradation process of each component, a reliability model of the repairable multi-component manufacturing tool system will be further developed."

Yang's research may lead to solutions for the auto industry in manufacturing energy saving auto structures. With improved reliability and failure prediction of the manufacturing tooling systems, manufacturers will be able to perform optimal maintenance planning and reduce tool failures, resulting in improved product quality in products and reduced manufacturing costs. The methodology is applicable for a wide range of industries such as aerospace/aircraft, machinery/machine tools, electronics and bio-devices.

Provided by Wayne State University

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