20.500.12556/DKUM-94362 Hardened workpiece shape prediction using acoustic responses and deep neural network This 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. metal workpiece hardened deep neural network acoustic respons shape prediction kovinski obdelovanec utrjanje globoke nevronske mreže akustični odzivi napovedovanje oblike true false true Springer Nature Angleški jezik Slovenski jezik https://link.springer.com/article/10.1007/s00170-025-16198-z Članek v reviji 2025-08-14 09:23:23 2025-08-14 09:23:24 2025-08-15 03:22:31 0000-00-00 00:00:00 2025 0 0 str. 5153-5161 iss. 9/10 Vol. 139 August 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-03-05 2025-07-21 2025-08-02 004.8:658.5 245609475 10.1007/s00170-025-16198-z 1433-3015 s00170-025-16198-z.pdf s00170-025-16198-z.pdf 1 30DBE77C00B3C84ED392DEFF120898F3 e5f79e40fdf82774a0daf7de95b763470a019e5ed92087eedf87d6cc88635f37 dcd89dd5-78df-11f0-a99e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=195304 https://link.springer.com/article/10.1007/s00170-025-16198-z 0 e432e595-78df-11f0-a99e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=195305 Fakulteta za strojništvo 0 0 0