Machine learning and Taguchi techniques for predicting wear mechanisms of Ni–Cu alloy composites

Abstract

Nickel–Copper alloys hybrid composite was formed in an induction furnace set up on a sand substrate. With different percentages of Al 2 O 3 (3, 6, 9 and 12 wt%) and TiO 2 (constant 9 wt%) reinforcements, the goal is to examine the wear behavior and friction coefficient of Ni–TiO 2 –Al 2 O 3 . The factors considered for the wear analysis were sliding distance (1500, 1000, and 500 m), applied load (25, 50, and 75 N), and sliding velocity (1.46, 2.93, and 4.39 m/s). The pin-on-disc equipment is utilized to perform different wear tests are carried out using in accordance with the Taguchi L27 orthogonal array. The machine learning used to correlate between actual and anticipated values for both metrics is strong, with a reasonable error margin. The Mean Squared Error (MSE) for the wear rate was 0.1025 (10.25%) in the Linear Regression model and 0.2390 (23.89%) in the Random Forest model. Regression analysis determined the impact of several parameters on wear rate, whilst machine learning approaches expanded the evaluation of wear rate and coefficient of friction beyond experimental data. The findings demonstrate the effectiveness of combining Taguchi methods with machine learning to accurately anticipate wear mechanisms in Ni–Cu alloy composites. • A Ni–Cu alloy composite was produced using Al₂O₃ (3–12 wt%) and TiO₂ (9 wt%). • Wear and friction of the Ni–TiO₂-Al₂O₃ composite were tested under varying conditions. • Wear tests used a pin-on-disc setup with the Taguchi L27 orthogonal array. • Machine learning models showed strong correlation with wear rate and friction data. • Taguchi methods and machine learning improved wear prediction in Ni–Cu alloy composites.

Topics & Concepts