Ren, J., Kang, Q., Feng, S., Sun, Y., Tan, Y. T., & Xiao, G. (2025). Machine learning-based multi-objective optimization framework for industrial black nickel electroplating. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-025-02573-w
Abstract:
The optimization of industrial processes, such as the industrial black nickel electroplating (IBNE) process, is challenging due to the intricate structure and dynamics involved. Machine learning (ML) methods are proposed to learn from historical data to address this issue. In this study, we propose a novel intelligent process optimization framework based on ML methods to optimize the IBNE process through a sim-to-real approach. The framework consists of a virtual IBNE environment simulator and a deep reinforcement learning-based optimization architecture. The virtual IBNE environment simulator is designed to
address three objectives: lightness, uniformity and plating rate, based on a historical plating dataset. Lightness and uniformity are considered in one defect detection problem, where a sample is classified as “Pass” if it meets both criteria, and “Fail”
otherwise. In addition, a reward function is formulated to evaluate the plating performance of samples, with the penalty term derived by solving a constrained polynomial optimization problem based on constraints extracted from the dataset. A deep deterministic policy gradient (DDPG) algorithm is presented to learn the optimal current density corresponding to different
plating conditions, ensuring that the plating process can achieve optimal performance under specific conditions. Finally, we
apply the policy learned in the virtual IBNE environment to a real-world application, and results from laboratory experiments validate the effectiveness of the proposed framework.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210112027
Description:
This is a post-peer-review, pre-copyedit version of an article published in Journal of Intelligent Manufacturing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10845-025-02573-w