Exploration of Machine Learning Techniques in AI-Driven Automation Process Optimization

This article explores the application of machine learning algorithms in automating process optimization. The study covers three main types of algorithms: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms, such as decision trees, random forests, and SVMs, excel in predictive maintenance and quality control. Unsupervised learning algorithms, including K-means clustering, DBSCAN, and PCA, are primarily used for process discovery, anomaly detection, and feature extraction. Reinforcement learning algorithms, such as Q-learning, deep Q-networks, and Actor-Critic methods, show advantages in optimizing dynamic environments, particularly in production scheduling and complex system control. The study indicates that selecting the appropriate method depends on the specific application.