Data-Driven Construction Method of Material Mechanical Behavior Model
Abstract
:1. Introduction
2. Ideas and Methods
- (1)
- The initial finite element model is established;
- (2)
- The effects of elastic modulus, yield strength, Poisson’s ratio, and other factors on stress–strain are studied, and then the decision variables and their value ranges are determined;
- (3)
- The method of random sampling is used to extract n groups of parameter combinations in the decision variable space;
- (4)
- The parameter combinations are substituted into finite element calculations to construct a sample set consisting of decision variables, stress and strain;
- (5)
- Based on the artificial neural network, the model of the mapping relationship between decision variables and stress and strain is trained separately;
- (6)
- The mechanical behavior is predicted with the established model, and the reliability of the model is verified by finite element calculation and experimental data.
3. Construction of Mechanical Behavior Model of Materials
3.1. Establishing Finite Element Model
3.2. Influence Analysis of Material Parameters
3.3. Obtain Training Sample Set
3.4. Obtain Mechanical Behavior Model
4. Verification of Mechanical Behavior Model
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, Y.; Ji, H.; Cai, Z.; Tang, X.; Li, Y.; Liu, J. Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation. Materials 2019, 12, 1893–1915. [Google Scholar] [CrossRef] [Green Version]
- Bobbili, R.; Madhu, V. Constitutive modeling and fracture behavior of a biomedical Ti–13Nb-13Zr alloy. Mater. Sci. Eng. A 2017, 700, 82–91. [Google Scholar] [CrossRef]
- Salvado, F.C.; Teixeira-Dias, F.; Walley, S.M.; Lea, Lewis, J.; Cardoso, J.B. A review on the strain rate dependency of the dynamic viscoplastic response of FCC metals. Prog. Mater. Sci. 2017, 88, 186–231. [Google Scholar] [CrossRef] [Green Version]
- Xin, X.; Zhao, W.; Xie, T. Analysis of compressive stress-strain curve of TB8 titanium alloy at room temperature. Forging and Stamping Tecnology. Forg. Stamp. Tecnol. 2014, 39, 126–130. [Google Scholar]
- Cheng, Y.; Liu, J.; Pan, J.; Meng, L.; Wang, H.; Mao, H.; Yang, J. Test method for obtaining dynamic mechanical parameters of metallic materials. China Meas. Test 2016, 42, 107–112. [Google Scholar]
- Gardner, L.; Yun, X. Description of stress-strain curves for cold-formed steels. Constr. Build. Mater. 2018, 189, 527–538. [Google Scholar] [CrossRef]
- Kweon, H.D.; Kim, J.W.; Song, O.; Oh, D. Determination of true stress-strain curve of type 304 and 316 stainless steels using a typical tensile test and finite element analysis. Nucl. Eng. Technol. 2021, 53, 647–656. [Google Scholar] [CrossRef]
- Zhao, K.; Wang, L.; Chang, Y.; Yan, J. Identification of post-necking stress–strain curve for sheet metals by inverse method. Mechanics of Materials. Mech. Mater. 2016, 92, 107–118. [Google Scholar] [CrossRef]
- Rickman, J.M.; Lookman, T.; Kalinin, S.V. Materials informatics: From the atomic-level to the continuum. Acta Mater. 2019, 168, 473–510. [Google Scholar] [CrossRef]
- Song, Z.; Chen, X.; Meng, F.; Cheng, G.; Wang, C.; Sun, Z.; Yin, W.-J. Machine learning in materials design: Algorithm and application. Chin. Phys. B 2020, 29, 116103–116132. [Google Scholar] [CrossRef]
- Liu, Y.; Niu, C.; Wang, Z.; Gan, Y.; Zhu, Y.; Sun, S.; Shen, T. Machine learning in materials genome initiative: A review. J. Mater. Sci. Technol. 2020, 57, 113–122. [Google Scholar] [CrossRef]
- Hart, G.L.W.; Mueller, T.; Toher, C.; Curtarolo, S. Machine learning for alloys. Nat. Rev. Mater. 2021, 6, 730–755. [Google Scholar] [CrossRef]
- Cruz, D.J.; Barbosa, M.R.; Santos, A.D.; Miranda, S.S.; Amaral, R.L. Application of Machine Learning to Bending Processes and Material Identification. Metals 2021, 11, 1418–1442. [Google Scholar] [CrossRef]
- Xie, J.; Su, Y.; Xue, D.; Jiang, X.; Fu, H.; Huang, H. Application of machine learning in material research and development. Acta Metall. Sin. 2021, 57, 1343–1361. [Google Scholar]
- Zhang, J.M.; Yang, W.D.; Li, Y. Application of artificial intelligence in composite material research. Advances in Mechanics. 2021, 51, 865–900. [Google Scholar]
- Naser, M.Z. Deriving temperature-dependent material models for structural steel through artificial intelligence. Constr. Build. Mater. 2018, 191, 56–68. [Google Scholar] [CrossRef]
- Jang, D.P.; Fazily, P.; Yoon, J.W. Machine learning-based constitutive model for J2- plasticity. Int. J. Plast. 2021, 138, 102919–102939. [Google Scholar] [CrossRef]
- Stoffel, M.; Bamer, F.; Markert, B. Artificial neural networks and intelligent finite elements in non-linear structural mechanics. Thin-Walled Struct. 2018, 131, 102–106. [Google Scholar] [CrossRef]
- Stoffel, M.; Bamer, F.; Markert, B. Neural network based constitutive modeling of nonlinear viscoplastic structural response. Mech. Res. Commun. 2019, 95, 85–88. [Google Scholar] [CrossRef]
- Sharath, B.N.; Venkatesh, C.V.; Afzal, A.; Aslfattahi, N.; Aabid, A.; Baig, M.; Saleh, B. Multi ceramic particles inclusion in the aluminium matrix and wear characterization through experimental and response surface-artificial neural networks. Materials 2021, 14, 2895–2919. [Google Scholar] [CrossRef]
- Nagaraja, S.; Kodandappa, R.; Ansari, K.; Kuruniyan, M.S.; Afzal, A.; Kaladgi, A.R.; Aslfattahi, N.; Saleel, C.A.; Gowda, A.C.; Bindiganavile Anand, P. Influence of heat treatment and reinforcements on tensile characteristics of aluminium AA 5083/Silicon Carbide/Fly ash composites. Materials 2021, 14, 5261–5288. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Shen, C.; Cui, Q.; Zhang, C.; Xu, W. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels. J. Nucl. Mater. 2020, 529, 151823–151842. [Google Scholar] [CrossRef]
- Guo, S.; Yu, J.; Liu, X.; Wang, C.; Jiang, Q. A predicting model for properties of steel using the industrial big data based on machine learning. Comput. Mater. Sci. 2019, 160, 95–104. [Google Scholar] [CrossRef]
- Merayo, F.D.; Rodríguez-Prieto, A.; Camacho, A.M. Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data. Metals 2020, 10, 904–933. [Google Scholar] [CrossRef]
- Loyola, R.D.; Pedergnana, M.; Gimeno, G.S. Smart sampling and incremental function learning for very large high dimensional data. Neural Netw. 2016, 78, 75–87. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of the International Conference on Parallel Problem Solving from Nature, Paris, France, 18–20 September 2000; Springer: Berlin/Heidelberg, Germany, 2000; pp. 849–858. [Google Scholar]
Group Number | Quantity/Points | /N | /MPa | |||
---|---|---|---|---|---|---|
1 | 100 | 0~100 | 400~700 | 1 × 104~1 × 105 | 0.001~0.01 | 0.1~0.45 |
2 | 100 | 0~100 | 400~700 | 1 × 104~1 × 105 | 0.01~0.1 | 0.1~0.45 |
3 | 100 | 0~100 | 400~700 | 1 × 104~1 × 105 | 0.1~0.3 | 0.1~0.45 |
4 | 100 | 0~100 | 400~700 | 1 × 105~1 × 106 | 0.001~0.01 | 0.1~0.45 |
5 | 100 | 0~100 | 400~700 | 1 × 105~1 × 106 | 0.01~0.1 | 0.1~0.45 |
6 | 100 | 0~100 | 400~700 | 1 × 105~1 × 106 | 0.1~0.3 | 0.1~0.45 |
7 | 100 | 100~1000 | 400~700 | 1 × 104~1 × 105 | 0.001~0.01 | 0.1~0.45 |
8 | 100 | 100~1000 | 400~700 | 1 × 104~1 × 105 | 0.01~0.1 | 0.1~0.45 |
9 | 100 | 100~1000 | 400~700 | 1 × 104~1 × 105 | 0.1~0.3 | 0.1~0.45 |
10 | 100 | 100~1000 | 400~700 | 1 × 105~1 × 106 | 0.001~0.01 | 0.1~0.45 |
11 | 100 | 100~1000 | 400~700 | 1 × 105~1 × 106 | 0.01~0.1 | 0.1~0.45 |
12 | 100 | 100~1000 | 400~700 | 1 × 105~1 × 106 | 0.1~0.3 | 0.1~0.45 |
13 | 100 | 1000~10,000 | 400~700 | 1 × 104~1 × 105 | 0.001~0.01 | 0.1~0.45 |
14 | 100 | 1000~10,000 | 400~700 | 1 × 104~1 × 105 | 0.01~0.1 | 0.1~0.45 |
15 | 100 | 1000~10,000 | 400~700 | 1 × 104~1 × 105 | 0.1~0.3 | 0.1~0.45 |
16 | 100 | 1000~10,000 | 400~700 | 1 × 105~1 × 106 | 0.001~0.01 | 0.1~0.45 |
17 | 100 | 1000~10,000 | 400~700 | 1 × 105~1 × 106 | 0.01~0.1 | 0.1~0.45 |
18 | 100 | 1000~10,000 | 400~700 | 1 × 105~1 × 106 | 0.1~0.3 | 0.1~0.45 |
19 | 100 | 10,000~25,000 | 400~700 | 1 × 104~1 × 105 | 0.001~0.01 | 0.1~0.45 |
20 | 100 | 10,000~25,000 | 400~700 | 1 × 104~1 × 105 | 0.01~0.1 | 0.1~0.45 |
21 | 100 | 10,000~25,000 | 400~700 | 1 × 104~1 × 105 | 0.1~0.3 | 0.1~0.45 |
22 | 100 | 10,000~25,000 | 400~700 | 1 × 105~1 × 106 | 0.001~0.01 | 0.1~0.45 |
23 | 100 | 10,000~25,000 | 400~700 | 1 × 105~1 × 106 | 0.01~0.1 | 0.1~0.45 |
24 | 100 | 10,000~25,000 | 400~700 | 1 × 105~1 × 106 | 0.1~0.3 | 0.1~0.45 |
Numbering | Simulation Stress/MPa | Experimental Stress/MPa | Error/% | Simulation Strain | Experimental Strain | Error/% |
---|---|---|---|---|---|---|
1 | 125.832 | 125.831 | −0.00079 | 0.00036 | 0.00036 | 0 |
2 | 174.898 | 174.926 | 0.016007 | 0.00061 | 0.00061 | 0 |
3 | 275.920 | 275.988 | 0.024639 | 0.00109 | 0.00111 | 1.801802 |
4 | 375.948 | 374.948 | −0.2667 | 0.00154 | 0.00156 | 1.282051 |
5 | 475.389 | 475.308 | −0.01704 | 0.00210 | 0.00213 | 1.408451 |
6 | 526.049 | 525.967 | −0.01559 | 0.00244 | 0.00248 | 1.612903 |
7 | 600.982 | 600.924 | −0.00965 | 0.00390 | 0.00391 | 0.255754 |
8 | 695.248 | 695.283 | 0.005034 | 0.02975 | 0.03005 | 0.998336 |
9 | 705.428 | 705.461 | 0.004678 | 0.03618 | 0.03619 | 0.027632 |
10 | 714.968 | 715.007 | 0.005454 | 0.04421 | 0.04424 | 0.067812 |
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Qu, M.; Li, M.; Wen, Z.; He, W. Data-Driven Construction Method of Material Mechanical Behavior Model. Metals 2022, 12, 1086. https://doi.org/10.3390/met12071086
Qu M, Li M, Wen Z, He W. Data-Driven Construction Method of Material Mechanical Behavior Model. Metals. 2022; 12(7):1086. https://doi.org/10.3390/met12071086
Chicago/Turabian StyleQu, Meijiao, Mengqi Li, Zhichao Wen, and Weifeng He. 2022. "Data-Driven Construction Method of Material Mechanical Behavior Model" Metals 12, no. 7: 1086. https://doi.org/10.3390/met12071086