Optimizing wear analysis of plasma sprayed Linz-Donawitz slag-Al2O3 coatings using experimental design and neural network. (September 2022)

• Record Type: Journal Article
• Authors: Pati, Pravat Ranjan; Satapathy, Alok; Gupta, Gaurav; Ray, Subhrajit
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 9(2022)
• Page Start: 1723
• Page End: 1736
• Publication Date: 2022-09
• Subjects: LD slag -- erosion wear -- plasma spraying -- taguchi technique -- ANN; Tribology -- Periodicals; 621.89
• DOI: 10.1177/13506501221106562
• Languages: English
• ISSNs: 1350-6501

Abstract

The present research investigates the erosion wear performance of plasma-sprayed Linz-Donawitz (LD) slag coatings through combined execution of experimental design and neural network. This effort reveals that LD slag is coatable on aluminum substrate. Different weight proportions of Al2 O3 are mixed with LD slag prior to coating deposition. In this investigation, it is observed that the coating thickness and micro-hardness improve with the addition of Al2 O3 into LD slag content. Wear characteristics of LD slag coatings in terms of parametric influence have been analyzed using Taguchi approach. Impact velocity is the most substantial for reducing the wear rate. The results are also optimized using artificial neural network (ANN). The experimental and ANN predicted data established a decent agreement keeping the error within 7%. The wear mechanism failures are also examined microscopically. This study demonstrates that these coatings are found appropriate in tribological areas as well.