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Direct current field enhanced boronizing of stainless steels and predictive performance of diffusion kinetics, deep neural network, and adaptive neuro-fuzzy inference system on boride layer thickness

  • Metals & corrosion
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Abstract

The boronizing process is a thermochemical surface treatment related to diffusion and requires high temperature and long-period soaking time. The applied direct current is a method that provides extra energy to the boronizing agent, and then the boronizing duration time shall be reduced. Moreover, predictive models of boronizing are recently essential and interesting for manufacturing. Therefore, powder-packed boronizing processes with and without an applied direct current field on AISI 420, 440C, and 304 stainless steels were investigated at 850–950 °C for about 2–6 h. Boride layer thicknesses were measured and predicted using kinetics and machine learning techniques, i.e., deep neural networks and adaptive neuro-fuzzy inference systems. The diffusion rate of boron atoms was enhanced, and the activation energies of powder-packed boronizing processes decreased if the direct current field was involved. The diffusion kinetic, deep neural networks, and adaptive neuro-fuzzy inference systems were efficient predictive tools for the boride layer thickness of powder-packed boronizing processes with and without an applied direct current field. Six input features, i.e., boronizing time, -temperature, Carbon-, Chromium-, Nickel contents, and condition of the direct current field, were used in the deep neural networks and adaptive neuro-fuzzy inference systems. The relative importance of input features was analyzed using the modified Garson’s algorithm. The three most critical input features were the boronizing time, -temperature, and direct current field. Modifying input features from six to four of the adaptive neuro-fuzzy inference systems decreased the complexity of the model but unaffected the predictive performance.

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Data availability

The datasets used and analyzed in the research are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors sincerely thank the Graduate School of Kasetsart University, Thailand, for financial support for Ms. Laksamee. Angkurarach.

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Authors and Affiliations

  1. Department of Materials Engineering, Faculty of Engineering, Kasetsart University, 50 Ngamwongwan Rd., Ladyao, Chatuchak, Bangkok, 10900, Thailand

    Patiphan Juijerm

  2. Division of Material and Process Engineering Technology, Faculty of Engineering and Technology, King Mongkut’s University of Technology North Bangkok, Rayong Campus, 19 Nonglalok, Bankhai, Rayong, 21120, Thailand

    Laksamee Angkurarach & Patcharin Naemchanthara

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  1. Patiphan Juijerm
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  3. Patcharin Naemchanthara
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PJ was involved in conceptualization, supervision, modelling analysis, and writing. LA was involved in conceptualization, experiments and data curation. PN was involved in conceptualization, experiments and data curation.

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Correspondence to Patiphan Juijerm.

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Juijerm, P., Angkurarach, L. & Naemchanthara, P. Direct current field enhanced boronizing of stainless steels and predictive performance of diffusion kinetics, deep neural network, and adaptive neuro-fuzzy inference system on boride layer thickness. J Mater Sci 58, 16507–16522 (2023). https://doi.org/10.1007/s10853-023-09072-4

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