Screw Process Anomaly Visualization (SPAV): A Python module for local and global machine learning visualizations for screw tightening anomaly detection

Abstract

Modern screwdriver systems generate real-time angle-torque data that form tightening curves that are valuable for quality inspection issues (e.g., detect faulty processes). This work describes the Screw Process Anomaly Visualization (SPAV) Python module, which provides several eXplainable AI (XAI) graphs for Machine Learning (ML) screw tightening results, namely global and local errors, with identification of most probable anomaly angle-torque locations. SPAV integrates seamlessly with the scientific Python ecosystem and is compatible with several ML implementations, including H2O and Keras deep AutoEncoders (AE).