Rehabilitation And Maintenance Of Rockwell Hardness Testing Machine Using Artificial Neural Network (Ann). a Case Study Of Safety In Caritas University Workshop Enugu

Abstract

The effective rehabilitation and maintenance of Rockwell Hardness Testing Machine is a generally used means in the field of material science and engineering. It evaluates the hardness of a material, which is a judgmental parameter in assessing its rightness for various applications. These instructions will provide a complete overview of how to operate a Rockwell Hardness Testing Machine efficiently.This study focuses on improving the rehabilitation and maintenance process of a Rockwell hardness testing in Caritas University Amorji Nike Emene, Enugu, using an Artificial Neural Network (ANN)-based technique. The research explores the application of ANN in predictive maintenance by analyzing historical operational data and identifying patterns that indicate potential faults or performance degradation.A structured methodology was adopted, involving data collection from the Rockwell hardness, preprocessing, and training of the ANN model. The ANN-based system was designed to predict faults and recommend maintenance actions before critical failures occur. Results from the study demonstrated that the ANN model accurately detected anomalies and provided timely alerts, significantly reducing downtime and improving operational efficiency. The findings highlight the potential of ANN-based techniques in transforming traditional maintenance practices into proactive and intelligent systems. The study recommends the integration of ANN systems into workshop maintenance frameworks, the training of technical personnel, and the adoption of sustainable practices to enhance reliability and productivity. This approach not only optimizes equipment performance but also contributes to the advancement of intelligent maintenance technologies in educational institutions.The recommendations provided are aimed at enhancing the effectiveness and scalability of the ANN-based rehabilitation and maintenance system at Caritas University Workshop. By addressing the challenges and leveraging the insights gained from this research, the institution can further optimize its equipment management, reduce operational risks, and enhance safety. Furthermore, these recommendations offer a pathway for broader implementation in other educational and industrial environments, contributing to the global movement toward smarter, AI-powered maintenance solutions.