The objective of this Grant Opportunities for Academic Liaison with Industry (GOALI) award is to use structural information of system components to improve prediction of reliability and failure in complex systems. The results of this project are expected to be of great benefit to the auto industry. In particular, improved reliability and failure prediction of manufacturing tooling systems will lead to more efficient and effective maintenance planning and reductions in tool failures. This, in turn, will lead to improvements in product quality and reduction in cost. Lab-enhancement and on-site studies will provide real-world experience and problem-solving skills for engineering education.
This research project deals with efficiently extracting material micro-structure characteristics and incorporating them into system reliability models to improve the accuracy of failure and reliability prediction. The new methodology will be applied, as an example, to the autobody manufacturing system of ultra-high strength steels, which is currently of great interest to the automotive industry but faces challenges in failure prediction and reliability analysis of manufacturing tool systems. Statistical methods will be developed to analyze and extract the micro-structure statistical characteristics and features of workpiece and tool materials that determine the strength competition and tool damage processes. In addition, a physical-statistical model that incorporates the extracted micro-structural features will be developed to describe the tool degradation process. Based on these, a reliability model of the repairable multi-component manufacturing tool system will be obtained. The research results will be implemented and validated through a three-stage process, including lab tests at the university lab, experiment and model validation using industry tryout, and validation and implementation in real manufacturing processes.
