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Prediction and optimization of electroplated Ni-based coating composition and thickness using central composite design and artificial neural network

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Abstract

The choice of the electroplating conditions of Ni-based alloys has always been a serious research question. In this study, an artificial neural network based on central composite design modelization were used to determine the desired percentage of Ni and the optimum thickness of the coating before passing to the implementation of the work. Three main factors were found to be very important in this process; namely applied current density (I), pH of the bath and the temperature (T) during electrolysis. The optimum conditions generated by the mathematical model proposed in this work were 42 mA cm−2, pH 4.5 and 50 °C for the Ni-alloys (Zn, Co, Cr and W). Theoretically, the optimum amount of Ni and the thickness of the alloy were 40% and 23 µm, respectively. The SEM images indicated that the optimum (I) would yield homogenous and compact morphologies. Moreover, the XPS investigations revealed that the optimum pH would form a strong Ni bond. Finally, the XRD analysis showed that the optimum T would result in a stable Ni-alloy crystallinity for Zn, Co and Cr. In contrast, Ni-W alloys showed that the amorphous phases were more stable.

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Authors and Affiliations

  1. Higher Institute of Applied Sciences and Technology of Gabes, Gabes University, Avenue Omar Ibn El Khattab, 6029, Gabes, Tunisia

    Wafa Sassi

  2. Unité de Recherche Electrochimie, Matériaux Et Environnement UREME (UR17ES45), Faculté Des Sciences de Gabès, Université de Gabès, Cité Erriadh, 6072, Gabès, Tunisia

    Wafa Sassi, Mouna Mrad & Salah Ammar

  3. Department of Chemistry, Gangadhar Meher University, Amruta Vihar, Sambalpur, Odisha, 768004, India

    Debasis Behera

  4. Institut UTINAM, CNRS UMR 6213, Univ Bourgogne Franche-Comté, 16 route de Gray, 25030, Besançon Cedex, France

    Jean-Yves Hihn

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  1. Wafa Sassi
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Sassi, W., Mrad, M., Behera, D. et al. Prediction and optimization of electroplated Ni-based coating composition and thickness using central composite design and artificial neural network. J Appl Electrochem 51, 1591–1604 (2021). https://doi.org/10.1007/s10800-021-01602-9

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