Extraction of the plastic properties of metallic materials from scratch tests using deep learning
Powered by machine learning and computer technology, neural networks have opened new paths for solving engineering problems. In this paper, the plastic parameters, i.e., the yield stress and strain hardening index, of metallic materials are extracted from scratch tests using deep learning methods. U...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
ELSEVIER
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/90385 |
| _version_ | 1848765375448088576 |
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| author | Zhang, J. Qin, J. Li, Y. Lu, Chunsheng Liu, H. Zhao, M. |
| author_facet | Zhang, J. Qin, J. Li, Y. Lu, Chunsheng Liu, H. Zhao, M. |
| author_sort | Zhang, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Powered by machine learning and computer technology, neural networks have opened new paths for solving engineering problems. In this paper, the plastic parameters, i.e., the yield stress and strain hardening index, of metallic materials are extracted from scratch tests using deep learning methods. Using a dataset generated by finite element simulations, three network models, i.e., the classical multi-output multi-layer perceptron (MLP), a single-target approach (ST-MLP) and the parameter sharing-based deep network (DMTR), are adopted to determine the relationship between scratch responses and plastic parameters. According to the test dataset results, the DMTR performs better than the MLP and ST-MLP. The trained DMTR is verified by comparing the plastic parameters of 18CrNiMo7-6 alloy steel, 304 stainless steel, and brass obtained from scratch tests with those under tension. This work is expected to provide an alternative method for determining the plastic parameters of metallic materials. |
| first_indexed | 2025-11-14T11:34:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-90385 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:34:15Z |
| publishDate | 2022 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-903852023-03-01T07:44:21Z Extraction of the plastic properties of metallic materials from scratch tests using deep learning Zhang, J. Qin, J. Li, Y. Lu, Chunsheng Liu, H. Zhao, M. Science & Technology Technology Materials Science, Multidisciplinary Mechanics Materials Science Plastic properties Scratch test Deep learning Finite element simulation Multi -target regression Neural networks SPHERICAL INDENTATION MECHANICAL-PROPERTIES INSTRUMENTED INDENTATION ELASTOPLASTIC MATERIALS MATERIAL PARAMETERS FRACTURE-TOUGHNESS ELASTIC-MODULUS HARDNESS MODEL WORK Powered by machine learning and computer technology, neural networks have opened new paths for solving engineering problems. In this paper, the plastic parameters, i.e., the yield stress and strain hardening index, of metallic materials are extracted from scratch tests using deep learning methods. Using a dataset generated by finite element simulations, three network models, i.e., the classical multi-output multi-layer perceptron (MLP), a single-target approach (ST-MLP) and the parameter sharing-based deep network (DMTR), are adopted to determine the relationship between scratch responses and plastic parameters. According to the test dataset results, the DMTR performs better than the MLP and ST-MLP. The trained DMTR is verified by comparing the plastic parameters of 18CrNiMo7-6 alloy steel, 304 stainless steel, and brass obtained from scratch tests with those under tension. This work is expected to provide an alternative method for determining the plastic parameters of metallic materials. 2022 Journal Article http://hdl.handle.net/20.500.11937/90385 10.1016/j.mechmat.2022.104502 English ELSEVIER restricted |
| spellingShingle | Science & Technology Technology Materials Science, Multidisciplinary Mechanics Materials Science Plastic properties Scratch test Deep learning Finite element simulation Multi -target regression Neural networks SPHERICAL INDENTATION MECHANICAL-PROPERTIES INSTRUMENTED INDENTATION ELASTOPLASTIC MATERIALS MATERIAL PARAMETERS FRACTURE-TOUGHNESS ELASTIC-MODULUS HARDNESS MODEL WORK Zhang, J. Qin, J. Li, Y. Lu, Chunsheng Liu, H. Zhao, M. Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title | Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title_full | Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title_fullStr | Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title_full_unstemmed | Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title_short | Extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| title_sort | extraction of the plastic properties of metallic materials from scratch tests using deep learning |
| topic | Science & Technology Technology Materials Science, Multidisciplinary Mechanics Materials Science Plastic properties Scratch test Deep learning Finite element simulation Multi -target regression Neural networks SPHERICAL INDENTATION MECHANICAL-PROPERTIES INSTRUMENTED INDENTATION ELASTOPLASTIC MATERIALS MATERIAL PARAMETERS FRACTURE-TOUGHNESS ELASTIC-MODULUS HARDNESS MODEL WORK |
| url | http://hdl.handle.net/20.500.11937/90385 |