Artificial neural network- multi-criteria decision making approaches to predict and validate the role of roughness parameters of D2 tool steel generated by various machining processes on friction and wear under dry and lubricated conditions

Abstract

Dies made of D2 tool steel commonly fail in heavy-duty cutting due to excessive wear from contact with sheet metal. Their wear resistance depends on steel composition, heat and surface treatment, and machining quality. Surface roughness from machining is a key factor in die failure. This study examines the impact of surfaces produced by various machining processes on the tribological properties of tool steel under lubricated and dry sliding conditions. Heat-treated D2 tool steel was machined using four processes—wire-cut electric discharge machining (WEDM), electric discharge machining (EDM), computer numerical control milling (CNC), and surface grinding (SG)—to generate surfaces with varying roughness. Surface roughness measurements revealed that CNC-milled surfaces had the highest roughness parameters, followed by SG, EDM, and WEDM. Based on the tribological results, artificial neural models (ANN) were used to predict the effect of roughness parameters on the coefficient of friction (COF) and specific wear rates (SWR). The ANN predictions were further validated using the novel MCDM approach. These approaches concluded that Ra, Rq, and Rv significantly influenced COF and SWR under lubricated conditions, whereas Ra, Rp, and Rv influenced the same under dry sliding conditions. Three-body abrasive wear was the dominant wear mechanism under both conditions. Additionally, micro-grooves were generated due to this wear mechanism under dry sliding conditions compared to lubricated ones.

Graphical Abstract

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