Vishesh Aggarwal, Tushar Patil, Vivek Patil, Ian Lockley
Abstract: Electro-dip coating (E-coating), also called Electrodeposition or ED-coating, is an economic technique for applying anti-corrosion paint coatings to electrically conducting materials. This process is widely used in the automotive industry to apply corrosion resistant coatings on automotive Body-in-White (BIW), where the BIW is dipped into an ED-tank carrying charged paint particles. The BIW itself acts as an electrode for an electrochemical reaction that results in a coat deposition on the BIW. While different dip motion tracks are employed by various manufacturers around the world, this study is performed using a downward tilted dip-in motion, followed by a rocking motion during in-tank travel and finally an upward tilted dip-out motion. During the dip-in motion, air bubbles tend to get trapped on the roof and interior cavities of the BIW, thereby adversely affecting the paint contact and deposition. Whereas during the dip-out motion, liquid paint tends to get trapped in pockets without drain holes and get carried on to the next stage of the process. Predicting such locations of air entrapment and liquid paint pockets can help examine and improve access/recess in the critical regions of BIW during the early stages of design process. Such study can be used to improve the coat process efficiency while meeting the quality criteria and reducing the waste and/or contamination by paint carryover on to subsequent paint shop processes. To perform such studies, we propose a simulation methodology using mesh motion techniques and an open channel boundary setup of the VOF model. The simulation speed is enhanced by using a hybrid non-iterative time advancement (hybrid-NITA) approach developed for the VOF model in Ansys Fluent 2021R1. The predictions from this technique are presented in comparison to those from an Overset Mesh motion approach and traditional iterative solvers in terms of speed and accuracy.
Keywords: E-dip, E-coat, BIW coating, paintshop, Overset.
Date Published: July 12, 2022 DOI: 10.11159/jffhmt.2022.007
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