GOALI: Failure Prediction and Reliability Analysis of Ultra-High Strength Steel Autobody Manufacturing Systems by Utilizing Material Microstructure Properties

GOALI学术联络机会奖的目的是利用系统部件的结构信息来提高对复杂系统的可靠性和故障的预测。预计该项目的成果将对汽车业产生巨大的好处。特别是，提高制造工装系统的可靠性和故障预测将导致更有效的维护计划和减少工装故障。这反过来将导致产品质量的提高和成本的降低。实验室强化和现场研究将为工程教育提供现实世界的经验和解决问题的技能。本研究项目致力于有效地提取材料的微观结构特征并将其纳入系统可靠性模型，以提高失效和可靠性预测的准确性。新方法将被应用于超高强度钢车身制造系统，这是目前汽车行业非常感兴趣的问题，但在制造工具系统的故障预测和可靠性分析方面面临挑战。将开发统计方法来分析和提取决定强度竞争和刀具损坏过程的工件和刀具材料的微观结构统计特征和特征。此外，还将开发一个包含所提取的微观结构特征的物理统计模型来描述刀具退化过程。在此基础上，建立了可修多部件制造工具系统的可靠性模型。研究成果将通过三个阶段进行实施和验证，包括在大学实验室进行实验室测试，使用行业试用进行实验和模型验证，以及在实际制造过程中进行验证和实施。

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.