| Title: | Hardened workpiece shape prediction using acoustic responses and deep neural network |
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| Authors: | ID Hernavs, Jernej (Author)
ID Peršak, Tadej (Author)
ID Brezočnik, Miran (Author)
ID Klančnik, Simon (Author) |
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| Files: |  s00170-025-16198-z.pdf (1,10 MB)
MD5: 30DBE77C00B3C84ED392DEFF120898F3
 https://link.springer.com/article/10.1007/s00170-025-16198-z
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | metal workpiece, hardened, deep neural network, acoustic respons, shape prediction |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 05.03.2025 |
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| Article acceptance date: | 21.07.2025 |
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| Publication date: | 02.08.2025 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 5153-5161 |
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| Numbering: | Vol. 139, iss. 9/10 |
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| PID: | 20.500.12556/DKUM-94362  |
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| UDC: | 004.8:658.5 |
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| ISSN on article: | 1433-3015 |
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| COBISS.SI-ID: | 245609475 |
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| DOI: | 10.1007/s00170-025-16198-z |
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| Publication date in DKUM: | 14.08.2025 |
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| Views: | 179 |
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| Downloads: | 14 |
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| Metadata: |    |
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| Categories: | Misc.
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