Abstract
Optimization of industrial processes is critical for improving efficiency, reducing costs, and enhancing product quality. Traditional optimization methods often struggle with the complexity and variability inherent in modern industrial systems. Machine learning (ML) techniques, however, offer a promising alternative by enabling systems to adapt, learn from data, and make real-time optimization decisions. This article explores the integration of machine learning-based optimization techniques in industrial processes, focusing on their applications in predictive maintenance, energy management, quality control, and supply chain optimization.
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