Effect of powder composition, PTAW parameters on dilution, microstructure and hardness of Ni-Cr-Si-B alloy deposition: Experimental investigation and prediction using machine learning technique

Chenrayan, Venkatesh and Shahapurkar, Kiran and Manivannan, Chandru and Rajeshkumar, L. and Sivakumar, N. and Sharma, R. Rajesh and Venkatesan, R. (2024) Effect of powder composition, PTAW parameters on dilution, microstructure and hardness of Ni-Cr-Si-B alloy deposition: Experimental investigation and prediction using machine learning technique. Heliyon, 10 (16). e36087. ISSN 2405-8440, DOI https://doi.org/10.1016/j.heliyon.2024.e36087.

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Official URL: https://doi.org/10.1016/j.heliyon.2024.e36087

Abstract

The implementation of hard-facing alloy on the existing materials caters the need for highperformance surfaces in terms of wear and high temperatures. The present research explore the effect of Plasma Transferred Arc Welding (PTAW) parameters and powder composition on dilution, microstructure and hardness of the commonly used hard-facing alloy Ni-Cr-Si-B powder. The hard-facing alloy was deposited with three weight proportions of boron (2.5 %, 3 % and 3.5 %). The statistical-based Grey Relational Analysis (GRA) followed by a Machine Learning Algorithm (MLA) was implemented to identify the ideal parameters and degree of significance of each parameter and for the prediction of the responses. The dilution percentage, microstructure analysis, and phase detection were estimated through elemental analysis, Scanning electron Microscopy (SEM) and X-ray Diffraction Analysis (XRD) respectively. The experimental and modelling results revealed that 400 mm/min of scanning speed, 8 gm/min of powder delivery, 14 mm of stand-off distance, and 120 A of current were the optimal parameters along with 3.5 wt% of boron powder composition to yield a better dilution, microstructure and hardness.

Item Type: Article
Funders: Alliance Univesity, Bengaluru
Uncontrolled Keywords: Chromium boride; Dilution; Grain growth; Heat affected zone; GRA; Machine learning
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering > Department of Mechanical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 12 Feb 2025 07:08
Last Modified: 12 Feb 2025 07:08
URI: http://eprints.um.edu.my/id/eprint/47489

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