Machine Learning in Precision Manufacturing: A Collaborative Computer and Mechanical Engineering Perspective

Abstract

Precision manufacturing, characterized by the production of intricate components with stringent tolerances, has witnessed a paradigm shift with the integration of machine learning (ML) techniques. This research paper delves into the collaborative endeavors between computer and mechanical engineering disciplines, exploring how the fusion of expertise from both domains contributes to the optimization and advancement of precision manufacturing processes. [1] The motivation behind this research stems from the recognition of ML's transformative potential in revolutionizing precision manufacturing. This paper reviews the application of ML in key areas such as predictive maintenance, process optimization, quality control, and adaptive manufacturing strategies. It emphasizes the synergy between computer and mechanical engineers in designing and implementing intelligent algorithms that harness data from various sensors, enabling real-time adjustments to manufacturing parameters. Predictive maintenance emerges as a critical aspect of  the collaborative efforts, where ML algorithms analyze sensor data to predict equipment failures, thereby reducing downtime and maintenance costs. Process optimization is explored through the integration of ML in Computer Numerical Control (CNC) machining, highlighting how algorithms optimize tool paths and enhance machining precision. Quality control in precision manufacturing is addressed by intelligent systems that utilize computer vision and sensor technologies, providing real-time defect detection. [2] The collaborative approach presents challenges, including data security and privacy concerns and the need for skillset integration between computer and mechanical engineering disciplines. The paper concludes by exploring future directions, including the integration of emerging technologies such as edge computing and the Internet of Things (IoT), underscoring the importance of interdisciplinary collaboration for the successful implementation of ML techniques in achieving heightened precision, efficiency, and quality in precision manufacturing.