The automotive paint shop is one of the most energy-intensive processes in vehicle manufacturing. This drives automotive OEMs to continuously improve production efficiency and reduce operational costs through digital twin technologies. Additionally, the push for shorter time-to-market emphasizes the need for simulation-based manufacturing processes—such as virtual testing and CAE simulations—to enable faster, data-driven decision-making early in the product development cycle, ultimately reducing cost and development time.
Among the various stages in the paint shop, two of the most critical 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 these processes, a Dip-Drain-E-Coating simulation was developed using Siemens Simcenter STAR-CCM+. This simulation replicates the E-coating process to provide insights into key operational challenges:
• During the dip-in phase, air may become trapped in the internal cavities of the BIW, preventing 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 is used to model the curing process. This helps identify underbaked or overbaked regions of the BIW by analyzing surface temperature distributions. Achieving thermal uniformity is essential to ensure that the primer forms a durable bond with the metal substrate, resulting in high-quality and long-lasting paint finishes.
This paper presents a simulation methodology that utilizes overset meshing and the multiphase Volume of Fluid (VOF) approach to model primer application in a cathodic E-coating process. Additionally, a conjugate heat transfer model is employed to simulate the baking process of a moving Body-in-White (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+ 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 was carried out to compare simulation outputs with physical test data. Through a teardown approach, an R² value of over 0.9 was achieved, 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+, supported by physical validation to ensure simulation accuracy.