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Although previous reviews of electroless nickel-based coatings have focused separately on deposition, coating performance or prediction methods, a thorough review that concurrently discusses the input process variables, output performance variables, performance prediction techniques and optimization strategies remains unaddressed. This study aims to provide a comprehensive review of the available literature on the performance prediction and optimization of electroless nickel-based coating, focusing on substrate types, coating types, input process variables, output performance variables, prediction methods and optimization techniques, including their strengths and weaknesses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used for the screening and selection phases of the study. Considering 32 studies, the most commonly employed input process variable and output performance variable were reducing agent concentration (24 studies) and microhardness of the coatings (10 studies) respectively. Real-world problems generally involve multiple output variables, but most prediction models were single-output. The majority of previous studies (15 studies) employed Artificial Neural Network (ANN) prediction models, which are black-box methods, limiting optimization due to their inability to generate explicit mathematical equations. Despite requiring predefined experimental designs, Response Surface Methodology (RSM) was the most widely used optimization strategy, accounting for 11 reviewed studies. This review guides researchers in establishing their study objectives and future research directions by addressing research gaps in the reviewed articles.
Lee et al. (Mon,) studied this question.