Abstract
An Automotive paint shop processes BIW through various stages to apply different paint layers like pretreatment, electro coat, primer and final coat. Pretreatment is a stage where high pressure water spray and dip tanks to clean the dirt or debris from the body shop. E-coat is an anti-corrosion coating where BIW is dipped through Electro Deposition (ED) tank for an electrochemical reaction. Paint defects like ‘non-painted areas will arise from air bubble entrapment in this process. Optimizing spray nozzle orientation based on experience lacks accuracy & prototyping with testing is very costly. And there is no predictive method to examine ED paint film thickness & to identify non-painted areas for BIW in design stage. It is expensive to cater late product design changes to overcome defects since paint thickness in the interior areas can only be verified once the BIW is torn down. Virtual engineering is a technology facilitating effective development and agile manufacturing through sophisticated computer models. In this paper, virtual engineering-based analysis is planned to, 1. Predict corrosion resistant paint film thickness on the car body through E-Coat Simulation, 2. Predict air entrapment and excess fluid retained in car body from dip process through Fluid access & drainage simulation (FAnD), & 3. Predict spray coverage on the car body surface along with liquid carryover in the car body to subsequent stages via fluid spray optimization study. Methodology developed to predict ED paint thickness, non-painted areas & spray coverage on BIW with liquid carryover is presented in this paper. The computational models were established from paint shop inputs to examine performance of ED paint thickness, non-painted areas & spray behavior. With the help of this study, it is now possible to virtually examine the E-coat coverage in critical regions (like interior cavities of BIW, where there are minimum or no access holes). This study also presents volume of air entrapment (mm3) and excess fluid left over volume (mm3) in car body from dip tank. This study has limitation to get accurate data inputs from the plant (from non-simulation engineer) which will effect calibration. It is not feasible to achieve 100% correlation to actual results as the developed physics is based on initial assumptions. Inputs has to be collected every time when there is a change in paint characteristics. This study offers an effective way of utilizing virtual method to improve the current process in paint shop. This methodology developed is unique and has a benefit of performing proposed types of simulation in single tool StarCCM+. This study also helps to improve quality and saves cost by avoiding late design changes.