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Multi-disciplinary design optimization in the context of a smart manufacturing environment

Author: 
Kyoung-Yun Kim (Wayne State University)

CHALLENGE - The Wayne State team is realizing a connected Resistance Spot Welding (RSW) weldability certification concept. RSW is one of the critical and common joining methods in sheet metal-based industries (e.g., automobile, electronics, and aircraft manufacturing). Certification of RSW weldability is crucial to validate the quality and safety of final products. However, the current RSW certification process has multiple challenges. The first challenge is that it is difficult to predict the weldability of new (or combination of) materials that are constantly required in order to satisfy new product functionalities. The second challenge is that a significant number of physical tests are required to certify a welding process for the new combination of materials. In the auto industry as an example, one weld design can require 300-600 tests to certify weldability. Thus, feasibility decisions can be delayed by 8-10 weeks.

SOLUTION - With the aid of modeFRONTIER, a connected platform is under development to integrate RSW data driven prediction systems, physical models, in-situ RSW sensors, and RSW weld quality metrics for real-time learning to make decisions per individual RSW sample.

BENEFITS - Use cases showing the multiple criteria optimization to estimate the welding control parameters are presented in the context of the connected weldability certification concept.

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