Description
Anomaly detection in manufacturing involves using machine learning models to identify irregularities that deviate from standard production patterns. By continuously monitoring production data and flagging anomalies, these systems can prevent defects, reduce waste, and maintain consistent product quality. Machine learning algorithms can detect subtle variations that may indicate equipment malfunctions, material inconsistencies, or process deviations, enabling rapid response to potential issues.
