Abstract
Industrial process optimization has entered a transformative era, driven by advancements in machine learning (ML). By integrating ML algorithms into industrial systems, manufacturers can realize unprecedented improvements in efficiency, accuracy, and adaptability. From predictive maintenance and quality assurance to process control and energy management, ML offers dynamic decision-making capabilities based on historical and real-time data. This paper explores the fundamental applications of ML in industrial environments and addresses key challenges while highlighting future prospects for smart manufacturing.
All articles published in the American Journal of Industrial and Production Engineering (AJIPE) are distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to copy, distribute, remix, adapt, and build upon the work, even commercially, as long as proper credit is given to the original author(s) and source. Authors are responsible for ensuring that their submissions do not infringe on any third-party copyrights and that all necessary permissions for copyrighted material have been obtained.