Numerical Strategy to Optimize Barrel Electroplating : Discrete Element Method and Electrochemical Modeling

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Electroplating barrel processes are widely used, particularly for corrosion protection of small components like screws, or gold plating applied to micro components. Achieving uniform coating for all parts is essential, requiring the identification of optimal parameters such as applied current, barrel rotation speed, or charge rate. The main goal is to maximize anode exposure times and reduce the risks of shocks or part sticking, which can result in heterogenous thickness distribution and aesthetic defects. Traditionally, the definition of optimal process parameters is on empirical approaches, with inherent limitations. This conference aims to present how complementary simulation methods can enhance the identification of optimal processing conditions, focusing on two key aspects.The first aspect involves modeling the electrochemical kinetics of the process to understand the current distribution due to the geometry part itself and predict thickness distribution or alloy composition in the case of an alloy.The second aspect explores the use of granular simulation with the Discrete Element Method (DEM), leveraging the computational power of GPU technology. Although DEM is known for its high computational cost, advancements in GPU computing have made it feasible to distribute computations across multiple cores, enabling faster simulations with larger particle counts. This is particularly beneficial for simulating barrel finishing processes, where complex shapes such as screws or bolts need to be accurately represented. By simulating the movement and interactions of these particles within the barrel, it can be possible to explore mixing dynamics and optimize charge rates to improve treatment uniformity.To validate the numerical models developed using these simulation methods, reduced-scale experimental tests are conducted. A 3D printed transparent drum is used to facilitate tracking of particle movement and interactions during the treatment process. This experimental validation provides crucial insights into the accuracy and reliability of the numerical models, ensuring that the simulated results align with experimental observations.We will also discuss numerical aspects and challenge in order to link DEM mixing results with electrochemical modeling.The results of this study enable reduction of quality defects with mixing and charge rates during electrolytic barrel treatment. The combination of electrochemical modeling and granular simulation allows for a better understanding of the link between process parameters and coating properties (thickness, composition...).In conclusion, the integration of advanced simulation methods offer potential numerical help to increase efficiency and reduce non-quality of barrel electroplating processes.