“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.
The award number for this National Science Foundation award is NSF CMMI-1404276.
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