Machine learning-driven detection of anomalies in manufactured parts from resonance frequency signatures

This study aims to enhance the detection and characterisation of anomalies in manufactured parts by integrating machine learning (ML) with resonance frequency spectra data. A key contribution of this work is the development of a novel Impulse Excitation Technique (IET)-based method that effectively evaluates material health and identifies subtle defects by leveraging numerous mathematical and physical metrics as input features. Three machine learning models – Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-layer Perceptron (MLP) – were systematically compared to determine the most effective method for classifying defects, specifically focusing on healthy, cracked, and dimensionally deviated samples. Among these, the MLP model demonstrated the highest performance, achieving Receiver Operating Characteristic (ROC) values of 0.963, 0.901, and 0.942 for each class, respectively. Additionally, SHAP (SHapley Additive exPlanations) analysis showed anomalies were sensitive to specific resonance frequency metrics, improving prediction accuracy. Cracked samples exhibited slight peak broadening and negative peak shifts, while dimensionally deviated samples showed positive and negative shifts and missing peaks. Dimensional deviations were more pronounced than cracks, making them easier to identify and enhancing predictive accuracy.