The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters
The indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is emplo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/12/4139 |
| _version_ | 1797484975456518144 |
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| author | Beata Potrzeszcz-Sut Agnieszka Dudzik |
| author_facet | Beata Potrzeszcz-Sut Agnieszka Dudzik |
| author_sort | Beata Potrzeszcz-Sut |
| collection | DOAJ |
| description | The indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is employed for estimating the selected material parameters described by Ramberg–Osgood’s law, i.e., Young’s modulus, the yield point, and the material hardening exponent. Two combined identification methods were used: the <i>P-A</i> procedure, in which the material parameters are identified on the basis of the coordinates of the indentation curves, and the <i>P-C</i> procedure, which uses the coordinates describing the imprint profile. The inverse problem was solved by neural networks. The results of numerical indentation tests—pairs of coordinates describing the indentation curves and imprint profiles—were used as input data for the networks. In order to reduce the size of the input vector, a simple and effective method of approximating the branches of the curves was proposed. In the Results Section, we show the performance of the approximation as a data reduction mechanism on a synthetic dataset. The sparse model generated by the presented approach is also shown to efficiently reconstruct the data while minimizing error in the prediction of the mentioned material parameters. Our approach appeared to consistently provide better performance on the testing datasets with considerably easier computation than the principal component analysis compression results available in the literature. |
| first_indexed | 2024-03-09T23:12:11Z |
| format | Article |
| id | doaj.art-db0aeeabb5ae4735866e374e87ba911e |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T23:12:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-db0aeeabb5ae4735866e374e87ba911e2023-11-23T17:43:16ZengMDPI AGMaterials1996-19442022-06-011512413910.3390/ma15124139The Application of a Hybrid Method for the Identification of Elastic–Plastic Material ParametersBeata Potrzeszcz-Sut0Agnieszka Dudzik1Department of Mechanics, Metal Structures and Computer Methods, Faculty of Civil Engineering and Architecture, Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, PolandDepartment of Mechanics, Metal Structures and Computer Methods, Faculty of Civil Engineering and Architecture, Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, PolandThe indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is employed for estimating the selected material parameters described by Ramberg–Osgood’s law, i.e., Young’s modulus, the yield point, and the material hardening exponent. Two combined identification methods were used: the <i>P-A</i> procedure, in which the material parameters are identified on the basis of the coordinates of the indentation curves, and the <i>P-C</i> procedure, which uses the coordinates describing the imprint profile. The inverse problem was solved by neural networks. The results of numerical indentation tests—pairs of coordinates describing the indentation curves and imprint profiles—were used as input data for the networks. In order to reduce the size of the input vector, a simple and effective method of approximating the branches of the curves was proposed. In the Results Section, we show the performance of the approximation as a data reduction mechanism on a synthetic dataset. The sparse model generated by the presented approach is also shown to efficiently reconstruct the data while minimizing error in the prediction of the mentioned material parameters. Our approach appeared to consistently provide better performance on the testing datasets with considerably easier computation than the principal component analysis compression results available in the literature.https://www.mdpi.com/1996-1944/15/12/4139parameter identification of material modelinverse analysisindentation testindentation curveimprint profileartificial neural networks |
| spellingShingle | Beata Potrzeszcz-Sut Agnieszka Dudzik The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters Materials parameter identification of material model inverse analysis indentation test indentation curve imprint profile artificial neural networks |
| title | The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters |
| title_full | The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters |
| title_fullStr | The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters |
| title_full_unstemmed | The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters |
| title_short | The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters |
| title_sort | application of a hybrid method for the identification of elastic plastic material parameters |
| topic | parameter identification of material model inverse analysis indentation test indentation curve imprint profile artificial neural networks |
| url | https://www.mdpi.com/1996-1944/15/12/4139 |
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