Computational Investigation of the Effect of Microstructure on the Abrasive Wear-Resistance of Nickel- Tungsten Carbide Composite Coatings

Sliding wear was simulated for tungsten carbide-nickel (WC-Ni) composite coatings with different WC particle sizes and volume fractions under single and multiple cyclic loadings. The effects of normal force, WC particle size, WC particle volume fraction, and their interaction on the worn volume and the material removal mechanisms were analyzed in WC-Ni metal matrix composite coating materials. This allowed competition and transition between microploughing, microcutting, and microfatigue to be investigated. The microploughing and microcutting mechanisms of material removal were identified and analyzed under a single abrasive particle scratch test using Artificial Neural Network, a machine learning approach. Under sub-critical dynamic cyclic loading, the composite coating with different WC particle sizes and volume fractions were analyzed using response surface methodology (RSM). In the single abrasive particle test, the results revealed that the material removal mechanism changes from microploughing to microcutting with an increase in particle volume fraction. Simulation of abrasive wear under sub-critical dynamic cyclic loading improved the understanding of microfatigue mechanisms of material removal. Altogether, the finite element and neural network modelling results reveal insights into the microstructure- and load-dependent failure processes in metal-matrix composite coatings that can be used in the future to design coatings with improved wear performance.