In automotive vehicle manufacturing, paint shop constitutes one of the highest energy intensive processes. This steers automotive OEMs to continuously improve production efficiency and reduce operational costs of the processes involved in paint shop through digital twin technologies. In addition, the push for shorter time-to-market emphasizes the need for simulation-based manufacturing processes, such as virtual testing and CAE simulations. The simulation-based processes enable faster and data-driven decision-making early in the product development cycle, thereby ultimately reducing cost and development time.
Among the various stages in the paint shop, two of the important stages are:
- 1
Electro-dip coating (E-coating), also known as Electro-Deposition coating, which applies a corrosion-resistant primer to the Body-in-White (BIW).
- 2
Oven curing, which ensures the primer is properly bonded and cured for long-term protection and finish quality.
To optimize the processes in these stages, the simulation models the stage of Dip-Drain-E-Coating using Simcenter™ STAR-CCM+™. This simulation replicates the E-coating process to provide insights into key operational challenges:
During the dip-in phase, air can become trapped in the internal cavities of the BIW, which prevents proper paint deposition. The simulation predicts potential air entrapment zones, ensuring uniform coating coverage and strong adhesion of the protective layer.
During the dip-out phase, residual paint can become trapped in recesses and carried into downstream stages. The simulation helps identify carryover zones and guides the optimal placement of drain holes and flow paths to promote effective draining.
It also evaluates coating thickness uniformity, which is crucial for consistent corrosion protection across all BIW surfaces.
Following E-coating, an oven simulation models the oven curing process. The oven simulation identifies underbaked or overbaked regions of the BIW by analyzing surface temperature distributions. Achieving thermal uniformity ensures that the primer forms a durable bond with the metal substrate, resulting in a high-quality and long-lasting paint finish.
This paper presents a simulation methodology applied on automotive Body-in-White (BIW) that utilizes overset meshing and multiphase Volume of Fluid (VOF) approach to model primer application in a cathodic E-coating process. Additionally, a conjugate heat transfer model simulates the baking process of a moving BIW inside a convection oven. The methodology enables accurate prediction of coating thickness and surface temperature, which are critical for effective curing, corrosion protection, and overall coating quality. Simcenter™ STAR-CCM+™ software is used for virtual paint shop simulations, focusing on important parameters like paint layer thickness and Body-in-White (BIW) temperature profiles. A validation study compares simulation outputs with physical test data. Using a teardown approach, the simulation results yield an R2 value of over 0.9, indicating a strong correlation between simulation results and real-world measurements.
This work demonstrates a digital twin of the paint shop process including dip coating and oven baking using Simcenter™ STAR-CCM+™ software. Physical validation supports the simulation to ensure accuracy.