Acoustic deep learning for defect detection in aluminium wheel rims.

Reliable detection of structural defects in aluminium wheel rims remains challenging in routine workshop diagnostics, where inspection is primarily based on visual and organoleptic assessment supported by operator experience. This study proposes RimNet, a deep-learning-based diagnostic framework for automated condition assessment of aluminium wheel rims using their acoustic response to a single impulse excitation. The method combines short-time Fourier transform representations with a convolutional neural network and incorporates domain filtering and hard-negative mining to improve the separability of defect-related conditions. The dataset comprised 930 labelled examples representing a broad spectrum of real-world rim states, including geometric deviations, material defects, and serviceable conditions. RimNet achieved strong classification performance, reaching 96.9% accuracy at the file level, while no wheel-level misclassifications were observed under the adopted evaluation protocol. Analysis of uncertainty measures-including prediction margins, entropy, Brier score, and energy-based indicators-revealed clear relationships between prediction stability and defect type. Misclassified cases were associated with reduced classification margins and increased uncertainty, particularly in samples influenced by geometric variability. The RimNet Predictor application enables real-time diagnostics and provides uncertainty estimates for individual rims. Because the proposed approach relies solely on microphone-based acquisition and does not require modifications to wheel balancing equipment, it can be readily integrated into workshop and service environments. These results demonstrate the potential of acoustic deep-learning-based diagnostics for structural health monitoring of lightweight mechanical components.