An Image-Driven Uncertainty Inverse Method for Sheet Metal Forming Problems

Abstract

The purpose of this study is to obtain a margin of safety for material and process parameters in sheet metal forming. Commonly applied forming criteria are difficult to comprehensively evaluate the forming quality directly. Therefore, an image-driven criterion is suggested for uncertainty parameter identification of sheet metal forming. In this way, more useful characteristics, material flow, and distributions of safe and crack regions, can be considered. Moreover, to improve the efficiency for obtaining sufficient statistics of Approximate Bayesian Computation (ABC), a manifold learning-assisted ABC uncertainty inverse framework is proposed. Based on the framework, the design parameters of two sheet metal forming problems, an air conditioning cover and an engine inner hood, are identified.