Multi-Class Error Detection in Industrial Screw Driving Operations Using Machine Learning

Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised methods to detect novel patterns. This paper introduces a comprehensive dataset of screw driving operations encompassing 25 distinct error types and presents a multi-tiered analysis framework for error-specific classification. Our results demonstrate varying detectability across different error types and establish the feasibility of multi-class error detection in industrial settings. The complete dataset and analysis framework are made publicly available to support future research in manufacturing quality control.