Curating Datasets of Flexible Assemblies to Predict Spring-Back Behavior for Machine Learning Purposes

Abstract

There are many sources of manufacturing variations in sheet metal assemblies, such as automotive bodies. These include non-isotropic material properties from cold rolling, springback in stamping, and distortion from residual stresses when components are clamped and spot welded. FE simulations have been used to predict these variations in order to better design tooling and processes. Such simulations require expertise in complex, multi-stage nonlinear analysis. We are investigating the feasibility of training machine learning algorithms in order to democratize these types of analyses. This requires the curation of large, validated, and balanced data sets. To this end, we have developed a multi-stage finite element simulation workflow encompassing component stamping and joining with a focus on examining deformations due to springback in two-part assemblies. Three connected simulations comprise the workflow: (1) component stamping with capture of springback, (2) assembly clamping, and (3) assembly joining, then release. The workflow utilizes explicit dynamic finite element analysis (FEA) and includes the transfer of intermediate solutions (geometries/stresses), as well as extraction of key geometric parameters of springback from both component- and assembly-level simulations. The NUMISHEET 1993 U-draw/bending benchmark was referenced for its tooling geometry and utilized for verification of the forming process simulation; variations of material and geometry were also simulated. In summary, this work provides a means of generating a design space of flexible two-part assemblies for applications such as dataset generation, design optimization, and machine learning.