Robustness and sensitivity analysis of a virtual process chain using the S-rail specimen applying random fields

T Konrad,S Wolff,K Wiegand,M Merklein

doi:10.1088/1742-6596/734/3/032126

Abstract

An important part in robustness evaluation of production processes is the identification of shape deviations. A systematic approach is typically based on the numerical evaluation of a DoE and the application of metamodels. They provide knowledge on solver noise and sensitivities of individual model parameters. This article presents the sensitivity analysis workflow of a linked deep drawing and joining process chain. LS-DYNA®, optiSLang and SoS is used. The challenge is to separate simulative from process and material parameters of AA 6014. Spatial quantities like variations in geometry, thinning and strain have to be considered in the next process steps. At the same time the number of required virtual CAE model evaluations must be limited. The solution is based on nonlinear metamodels and random fields.

Highlights

Due to increasing customer requirements regarding the features of both manufactured parts and assemblies, production processes have to be robust during product life cycles
Results of the Robustness Analysis of the Joining process Compared to the Forming Process the part and assembly designers are interested in the most significant influencing parameters on the shape deviation of the single and assembled parts
At a rate of 66 %, the E-modulus of the outer part exerts the highest influence on the z-deviation of the nominal geometry after the forming process

Summary

Introduction

Due to increasing customer requirements regarding the features of both manufactured parts and assemblies, production processes have to be robust during product life cycles. Car manufactures pay attention to produce add-on parts of highly dimensional accuracy and especially body-in-whites with equal gap dimensions [1] In addition to these standards of quality, the necessary production efficiency and flexibility with respect to several product variants lead to a higher complexity of the entire process chain. The outcome of a probabilistic forming simulation is random for all elements, the variations of result quantities can be described through a small number of random parameters known as “random field amplitudes”. These parameters are reused to generate new field variations in subsequent process steps.

Methods and Setup for the Robustness Evaluation

Results of the Robustness Analysis of the Simulative Process Chain

Conclusion