Improving Application Of Ergonomics In Engineering Machine Design Using Ann Based System

Abstract

The integration of ergonomics into engineering machine design is essential for enhancing user comfort, reducing workplace injuries, and improving operational efficiency. Traditional ergonomic assessments often rely on manual evaluations and predefined models, which may not fully capture the complexity of human-machine interactions. This study explores the application of Artificial Neural Networks (ANN) to optimize ergonomic design in engineering machines. ANN-based systems leverage intelligent learning algorithms to analyze human factors, predict ergonomic risks, and recommend design improvements. By incorporating real-time data processing and adaptive learning, ANN enhances decision-making in ergonomic assessments, leading to safer and more efficient machine designs. The research examines key ergonomic parameters, including posture analysis, force exertion, and user adaptability, utilizing ANN to refine design configurations. The proposed approach aims to bridge the gap between conventional ergonomic methods and intelligent automation, ultimately contributing to a more effective and user-centered engineering design process. This study highlights the potential of ANN-based systems in revolutionizing machine ergonomics, fostering enhanced productivity, and promoting workplace safety. The conventional Resistance to Change that cause poor application of ergonomics in engineering machine design was 15%. Meanwhile, when an ANN based system was imbibed into the system, it reduced it to13.4%. Finally, the percentage improvement in application of ergonomics in engineering machine design when an ANN based system was applied was 1.6%.