The Application of Machine Learning for Enhancing Process Control in U.S. Manufacturing Supply Chains
Keywords:
Process Control, . Manufacturing Supply ChainsAbstract
Process control is a critical aspect of ensuring efficient and effective production within manufacturing supply chains. It encompasses the methods and technologies used to monitor and regulate the various stages of production to maintain quality, consistency, and safety. Process control plays a pivotal role in minimizing waste, optimizing resource utilization, and meeting production targets. Additionally, it is essential for ensuring compliance with safety regulations and quality standards, thereby impacting the overall operations of a manufacturing supply chain [1].
In the context of (bio)chemical processes, machine learning applications have been proposed to enhance process control by addressing uncertainties in data, ensuring robustness and safety guarantees, and supporting controller design. These applications aim to integrate with hierarchical control structures and utilize data for system identification, state and parameter estimation, and monitoring, ultimately improving control performance and safety. Similarly, in semiconductor manufacturing, reinforcement learning techniques have been explored for adaptive run-to-run optimization, robust multistage process control, and quality improvement, highlighting the diverse applications of machine learning in enhancing process control within manufacturing supply chains [2].
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