Neural Network Approach on Influence of Ceramics on Dry Sliding Wear in Al-Cu-Zr Metal Matrix Composite

Abstract

This research aims to investigate the dry sliding wear behavior of Al-Cu-Zr (ACZ) metal matrix composite (MMC) at various Aluminium oxide (AOX) Nano particles compostion. ACZ alloy is widely used in on road and space mobility applications where the focus is on wear resistance. To enhance the resistance towards wear, Al alloy is reinforced with AOX nanoparticles at 3%, 6%, and 9% addition using stir casting process. The wear assessment is conducted at varying Composition (COMP), load (LD), speed (Ns), and sliding distance (SLDN). The wear rate (WRT) and Frictional force (FRFC) are analysed for different process parameters. To optimize the experiments, Taguchi signal-to-noise ratio (STNR) is used. Taguchi analysis show that the optimal conditions for minimum WRT and FRFC are at 6% AOX addition, 12.5 N load, 500 rpm speed, and 35 mm SLDN. Furthermore, an artificial neural network model (ANNM) is developed to forecast the WRT and FRFC. The neural network model is trained using the experimental data and the optimized process parameters. The neural network is a powerful tool that can learn the complex relationship between input and output variables. The model is validated using the experimental data, and the results show that the neural network model can predict the WRT and coefficient of friction with high accuracy. The Taguchi optimization and neural network model can provide a systematic approach to optimizing the process parameters and predicting the WRT and coefficient of friction. This approach can be applied to other materials and processes to improve their performance and reduce costs.