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The optimization of nickel electroplating process parameters with artificial intelligence methods

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Abstract

The scope of this study is to estimate the composition of the nickel electrodeposition bath using artificial intelligence method and optimize the organic additives in the electroplating bath via NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization algorithm. Mask RCNN algorithm was used to classify the coated hull-cell panels based on their appearance. The coated plate was divided into 5 classes as “Full Bright”, “Full Fail”, “HCD Fail”, “LCD Fail” and “Metallic impurity”. The intersection over union (IoU) values of the Mask RCNN model was ascertained between 89 and 98%. Machine learning (ML) algorithms, MLP, SVR, XGBoost, GP and RF, were trained using the images classified by the Mask RCNN. The additives in the electrodeposition bath and operating parameters were specified as an input and the classes of the coated panels as an output in the ML study. Among the trained models, RF model imparted the highest F1 scores for all classes. The F1 scores of RF model for” Full Bright”, “Full Fail”, “HCD Fail”, “LCD Fail” and “Metallic impurity” are 0.99, 1.00, 1.00, 1.00 and 0.94 respectively. NSGA-II genetic algorithm was employed to optimize the content of the bath. The trained RF model was defined as the objective function in the optimization process. The organic additives and operating parameters for full bright coating surface were optimized and the direction and importance of features (factors) impacting the output were delineated.

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Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The experiments reported in this article were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources), some computing resources were provided by the National Center for High Performance Computing of Turkey (UHeM) and Lütfi Albay Artificial Intelligence and Robotic Laboratory in Sivas University of Science and Technology.

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Authors and Affiliations

  1. Metallurgical and Materials Engineering, Sivas University of Science and Technology, Sivas, Turkey

    Ramazan Katirci

  2. Electrical and Electronics Engineering, Sivas University of Science and Technology, Sivas, Turkey

    Kevser Irem Danaci

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  1. Ramazan Katirci
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  2. Kevser Irem Danaci
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Contributions

All authors contributed to the concept and design of the study. Data set preparation, coding process and necessary studies were done by Ramazan Katırcı and Kevser Irem Danaci. The first draft of the article was written by Ramazan Katırcı and Kevser Irem Danacı contributed during the writing process of the article. All authors have read and approved the final draft.

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Correspondence to Ramazan Katirci.

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Katirci, R., Danaci, K.I. The optimization of nickel electroplating process parameters with artificial intelligence methods. J Appl Electrochem 53, 2077–2089 (2023). https://doi.org/10.1007/s10800-023-01892-1

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  • DOI: https://doi.org/10.1007/s10800-023-01892-1

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