Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Wear of ceramic particle-reinforced metal-matrix composites

Wear experiments have shown that when the applied normal stress exceeds a critical value, a transition occurs from the regime of mild wear to that of adhesive wear. As a result, the wear rate of particle-reinforced aluminium composites increases by a hundred-fold. Based on dislocation and delamination theories, a criterion is proposed for determining the critical transition load, and a quantita...

Prediction Based Diagnosis Using Artificial Intelligence Methods

Prediction a of Tribocorrosion Wear Rate (RESEARCH NOTE)

Basic properties of the suggested phenomenological model of tribocorrosion wear are considered. Using a friction pair austenitic stainless steel-ceramic in acid electrolytes as an example, a good agreement between predicted data and experimental evidence was obtained.

Prediction of cutting forces in machining of metal matrix composites

This paper presents a mechanics model for predicting the forces of cutting aluminum-based SiC/Al2O3 particle reinforced MMCs. The force generation mechanism was considered to be due to three factors: (a) the chip formation force, (b) the ploughing force, and (c) the particle fracture force. The chip formation force was obtained by using Merchant’s analysis but those due to matrix ploughing defo...

Diagnosis of Pulmonary Tuberculosis Using Artificial Intelligence (Naive Bayes Algorithm)

Background and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive Bayes algorithm as a...