Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming
Forming simulation requires a constitutive model whose parameters are typically determined with tensile tests assumed static. However, this conventional approach is impractical for high-speed forming simulation characterized by high strain rates inducing transient effects. To identify constitutive p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/20/7179 |
| _version_ | 1797471778153431040 |
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| author | Dayoung Kang Hak-Gon Noh Jeong Kim Kyunghoon Lee |
| author_facet | Dayoung Kang Hak-Gon Noh Jeong Kim Kyunghoon Lee |
| author_sort | Dayoung Kang |
| collection | DOAJ |
| description | Forming simulation requires a constitutive model whose parameters are typically determined with tensile tests assumed static. However, this conventional approach is impractical for high-speed forming simulation characterized by high strain rates inducing transient effects. To identify constitutive parameters in relation to high-speed forming simulation, we formulated the problem of constitutive modeling as inverse parameter estimation addressed by regularized nonlinear least squares. Regarding the proposed inverse constitutive modeling, we adopted the <i>L</i>-curve method for proper regularization and model order reduction for rapid simulation. For demonstration, we corroborated the proposed strategy by identifying the modified Johnson–Cook model in the context of a free bulge test with electromagnetic metal forming simulation. The <i>L</i>-curve method allowed us to systematically choose a regularization parameter, and model order reduction brought enormous computational savings. After identifying constitutive parameters, we successfully verified and validated the reduced and original simulation models, respectively, with a manufactured workpiece. In addition, we validated the numerically identified constitutive model with a dynamic material test using a split Hopkinson pressure bar. Overall, we showed that inverse constitutive modeling for high-speed forming simulation can be effectively tackled by regularized nonlinear least squares with the help of an <i>L</i>-curve and a reduced-order model. |
| first_indexed | 2024-03-09T19:53:51Z |
| format | Article |
| id | doaj.art-29756ccd990c40e981292adb5cfa88cf |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T19:53:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-29756ccd990c40e981292adb5cfa88cf2023-11-24T01:03:32ZengMDPI AGMaterials1996-19442022-10-011520717910.3390/ma15207179Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal FormingDayoung Kang0Hak-Gon Noh1Jeong Kim2Kyunghoon Lee3Department of Aerospace Engineering, Pusan National University, Busan 46241, KoreaKorea Aerospace Industries, Sacheon 52537, KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, KoreaDepartment of Aerospace Engineering, Pusan National University, Busan 46241, KoreaForming simulation requires a constitutive model whose parameters are typically determined with tensile tests assumed static. However, this conventional approach is impractical for high-speed forming simulation characterized by high strain rates inducing transient effects. To identify constitutive parameters in relation to high-speed forming simulation, we formulated the problem of constitutive modeling as inverse parameter estimation addressed by regularized nonlinear least squares. Regarding the proposed inverse constitutive modeling, we adopted the <i>L</i>-curve method for proper regularization and model order reduction for rapid simulation. For demonstration, we corroborated the proposed strategy by identifying the modified Johnson–Cook model in the context of a free bulge test with electromagnetic metal forming simulation. The <i>L</i>-curve method allowed us to systematically choose a regularization parameter, and model order reduction brought enormous computational savings. After identifying constitutive parameters, we successfully verified and validated the reduced and original simulation models, respectively, with a manufactured workpiece. In addition, we validated the numerically identified constitutive model with a dynamic material test using a split Hopkinson pressure bar. Overall, we showed that inverse constitutive modeling for high-speed forming simulation can be effectively tackled by regularized nonlinear least squares with the help of an <i>L</i>-curve and a reduced-order model.https://www.mdpi.com/1996-1944/15/20/7179inverse identificationhigh-speed formingelectromagnetic metal formingregularized nonlinear least squaresmodel order reduction |
| spellingShingle | Dayoung Kang Hak-Gon Noh Jeong Kim Kyunghoon Lee Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming Materials inverse identification high-speed forming electromagnetic metal forming regularized nonlinear least squares model order reduction |
| title | Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming |
| title_full | Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming |
| title_fullStr | Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming |
| title_full_unstemmed | Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming |
| title_short | Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming |
| title_sort | inverse identification of a constitutive model for high speed forming simulation an application to electromagnetic metal forming |
| topic | inverse identification high-speed forming electromagnetic metal forming regularized nonlinear least squares model order reduction |
| url | https://www.mdpi.com/1996-1944/15/20/7179 |
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