Ahmadi, Elaheh, Gheibi, Mohammad, Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408, Moezzi, Reza and Annuk, Andres (2026) Optimization of wear-resistant pulse-electrodeposited Co–P multilayer coatings: experimental characterization and ANFIS–GA modeling. Journal of Materials Research and Technology, 41. ISSN 2238-7854

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Abstract

This study examines the mechanical and tribological performance of pulse-deposited multilayer Co–P coatings on low-carbon steel, combining experimental characterization with AI-based predictive modeling. Multilayer structures containing 64–1600 layers were fabricated via pulse electrodeposition using alternating 10 % and 90 % duty cycles to precisely control composition and thickness. FESEM/EDS, XRD, microhardness, fracture toughness, and ball-on-disc tests revealed marked improvements in hardness (up to 840 HV), reduced friction (COF: 0.93–0.46), and significantly enhanced wear resistance (minimum mass loss: 1.5 × 10−3 g). To extend experimental insights, an Adaptive Neuro-Fuzzy Inference System (ANFIS) accurately predicted mass loss and identified load as the dominant wear-controlling parameter, with layer thickness and layer count exerting secondary effects. A Genetic Algorithm (GA) optimization further suggested near-optimal conditions (14.66 N load, ∼1590 layers, ∼592 nm thickness) achieving the same minimal mass loss observed experimentally. Overall, the integrated AI framework not only reproduced experimental behavior but also provided predictive and optimization capability, offering a practical decision-support tool for designing advanced wear-resistant coatings.

Item Type:	Article
Identifier:	10.1016/j.jmrt.2026.01.197
Keywords:	Multilayer coating; Electrodeposition; Wear behaviour; Machine learning model; Evolutionary algorithm
Subjects:	Computing
Date Deposited:	29 Sep 2026
Dates:	Date Publication status 26 January 2026 Accepted 28 January 2026 Published Online
School, department or research centre:	School of Computing and Engineering
Keywords:	Multilayer coating; Electrodeposition; Wear behaviour; Machine learning model; Evolutionary algorithm
URI:	https://repository.uwl.ac.uk/id/eprint/15452
Sustainable Development Goals:	Goal 9: Industry, Innovation, and Infrastructure

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