Prediction of Thermoplastic Fender Behavior During E-coat Bake Cycle - Part 1: FEA Methodology and Problem Formulation 2010-01-0232

This paper presents a Finite element analysis (FEA) methodology to predict the behavior of an automotive thermoplastic fender subjected to the e-coat paint bake cycle. Such a methodology, essential for an optimum fender design involves solution of a thermo-viscoelastic problem whose solution is not yet reported in literature.

This FEA methodology employed in the early design phase would help in the development of an optimum thermoplastic fender and support strategy. It is shown with help of a case study that the efficacy of different support combinations and their effect on final fender deformations can be predicted virtually very early in the design phase. While this paper presents the methodology and its application using the example of a large body panel (BP) like fender, it can easily be applied for predicting the response of other thermoplastic parts like tailgate and tank flap during the paint cycle. In addition, the different material characterization steps and details of user defined *UTRS routine used for performing the viscoelastic stress analysis are also presented.

In this paper, we also identify that the displacement prediction is sensitive to several variables associated with the ecoat process and propose a comprehensive multi-physics solution framework to include them. Such a solution, requires consideration of the stress-history in the fender in the form of molded-in/residual stresses induced during manufacture and subsequent assembly stresses created during assembly with Body in white(BIW). An accurate prediction of heat transfer and temperature distribution in the fender during the e-coat bake cycle is required. In addition, an accurate viscoelastic material model for the PPO-PA6-PA66 blend is required. Given the vast scope of the problem we spilt the study in parts, Part 2 of the paper deals with the modeling of the convective heating and its effect on the deformation prediction. Part 3 presents the methodology to capture stress history.