Experimental investigation and mathematical modelling of dry sliding wear of nitinol reinforced aluminium matrix composite using machine learning

Abstract

Dry sliding wear is a complex phenomenon, an understanding of which needs careful experimentation and observation of the wear event. The present study focuses on the dry sliding wear analysis of the nitinol particulate-reinforced Al5083 matrix composite fabricated using a vacuum-assisted stir casting route. Nitinol alloys have excellent mechanical, thermo-mechanic and tribological performance along with the unique feature of shape recovery and superelasticity in the austenitic phase. The effect of varying wear control parameters, load (5–20 N), sliding distance (0–3000), sliding speed (150–600 RPM), humidity (20% – 80%) and temperature (25–100°C) on the wear resistance of the fabricated composites were exhaustively examined. Steady-state, as well as Taguchi-based experimentation, were performed to investigate the wear phenomenon in the fabricated composite using a pin-on-disc tribometer. Modified Archard equation with power exponent was utilized to develop four new wear equations, which were both trained and tested using a machine learning algorithm. The results depict that among all four models, the DSW Model-4 has provided the best fit between the actual and the predicted wear volume with a coefficient of determination value of 0.9722. An infrared thermal imager was used to observe temperature variation during the wear test. Scanning electron microscopy (SEM) and a profilometer were used to carefully examine the worn surface.

Graphical Abstract

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