Hardened workpiece shape prediction using acoustic responses and deep neural networkHernavs, Jernej (Avtor) Peršak, Tadej (Avtor) Brezočnik, Miran (Avtor) Klančnik, Simon (Avtor) metal workpiecehardeneddeep neural networkacoustic responsshape predictionThis study proposes a novel approach to predict the shape of hardened metal workpieces using acoustic responses processed by a deep convolutional neural network (CNN), aiming to advance automated straightening in manufacturing. Tool steel 1.2379 workpieces of varying widths (24 mm, 90 mm, 200 mm) were struck using a custom-built device, with acoustic responses captured and transformed into scalograms via Continuous Wavelet Transform (CWT). A 40-layer CNN predicted 5×9 shape matrices, validated by 3D scans. The dataset (219 shape states, 3396 recordings) was evaluated using leaveone-workpiece-out cross-validation, comparing the CNN against baseline models (linear regression, random forest, shallow CNN, XGBoost). CNN achieved competitive accuracy, demonstrating the feasibility of acoustic-based shape prediction. As a non-invasive, cost-efective complement to 3D scanning, this method ofers innovative potential for multi-modal quality control systems in manufacturing.Springer Nature20252025-08-14 09:23:23Članek v reviji94362UDK: 004.8:658.5COBISS_ID: 245609475DOI: 10.1007/s00170-025-16198-zISSN pri članku: 1433-3015sl