Formability Prediction of Anisotropic Thin Sheets Using Machine Learning Framework: Influence of Pre-Strain and Temperature
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OpenAlex topics: Metal Forming Simulation Techniques · Metallurgy and Material Forming · Manufacturing Process and Optimization
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References (82)
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1. G.M. Goodwin, Application of Strain Analysis to Sheet Metal Forming Problems in the Press Shop, Sae Trans., 1968, p 380-387.10.4271/680093 ↗
2. A. Graf and W.F. Hosford, Calculations of Forming Limit Diagrams, Metall. Trans. A, 1990, 21, p 87–94.10.1007/BF02656427 ↗
3. Z. Marciniak, K. Kuczyński, and T. Pokora, Influence of the Plastic Properties of a Material on the Forming Limit Diagram for Sheet Metal in Tension, Int. J. Mech. Sci., 1973, 15(10), p 789–800.10.1016/0020-7403(73)90068-4 ↗
4. J.M. Allwood and D.R. Shouler, Generalised Forming Limit Diagrams Showing Increased Forming Limits with Non-Planar Stress States, Int. J. Plast., 2009, 25(7), p 1207–1230. https://doi.org/10.1016/j.ijplas.2008.11.00110.1016/j.ijplas.2008.11.001 ↗
5. A. Graf and W. Hosford, Effect of Changing Strain Paths on Forming Limit Diagrams of AI 2008–T4, Metall. Trans. A, 1993, 24(11), p 2503–2512.10.1007/BF02646529 ↗
6. C. Chin-Chan, An Investigation of the Strain Path Dependence of the Forming Limit Curve, Int. J. Solids Struct., 1982, 18(3), p 205–215. https://doi.org/10.1016/0020-7683(82)90003-810.1016/0020-7683(82)90003-8 ↗
7. K. Bandyopadhyay, S. Basak, H. Choi, S.K. Panda, and M.-G. Lee, Influence of Evolution in Anisotropy during Strain Path Change on Failure Limits of Sheet Metals, Met. Mater. Int., 2021, 27, p 3225–3237.10.1007/s12540-020-00896-2 ↗
8. E. Affronti and M. Merklein, Analysis of the Bending Effects and the Biaxial Pre-Straining in Sheet Metal Stretch Forming Processes for the Determination of the Forming Limits, Int. J. Mech. Sci., 2018, 138, p 295–309.10.1016/j.ijmecsci.2018.02.024 ↗
9. H. Li, G. Li, G. Gao, W. Zhang, and X. Wu, A Formability Evaluation Method for Sheet Metal Forming with Non-Linear Strain Path Change, Int. J. Mater. Form., 2018, 11, p 199–211.10.1007/s12289-017-1342-y ↗
10. P. Lennemann, Y.P. Korkolis, and A.E. Tekkaya, Influence of Changing Loading Directions on Damage in Sheet Metal Forming, Adv. Ind. Manuf. Eng., 2024, 8, p 100139.
11. S. Basak, S.K. Panda, and M.-G. Lee, Formability and Fracture in Deep Drawing Sheet Metals: Extended Studies for Pre-Strained Anisotropic Thin Sheets, Int. J. Mech. Sci., 2020, 170, p 105346.10.1016/j.ijmecsci.2019.105346 ↗
12. D. Li and A. Ghosh, Tensile Deformation Behavior of Aluminum Alloys at Warm Forming Temperatures, Mater. Sci. Eng. A, 2003, 352(1–2), p 279–286.10.1016/S0921-5093(02)00915-2 ↗
13. H. Laurent, J. Coër, P.Y. Manach, M.C. Oliveira, and L.F. Menezes, Experimental and Numerical Studies on the Warm Deep Drawing of an Al-Mg Alloy, Int. J. Mech. Sci., 2015, 93, p 59–72. https://doi.org/10.1016/j.ijmecsci.2015.01.00910.1016/j.ijmecsci.2015.01.009 ↗
14. S.S. Panicker and S.K. Panda, Experimental and Numerical Studies on the Formability of AA5754 and AA6082 Thin Sheets in Nonisothermal Warm Redrawing Process, Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl., 2022, 236(1), p 155–165.
15. S.S. Panicker, K. Bandyopadhyay, and S.K. Panda, “Failure Prediction and Forming Behavior of AA5754 Sheets at Warm Temperature,” All India Manufacturing Technology, Design and Research conference, 2018, p 53–65.10.1007/978-981-32-9417-2_4 ↗
16. T. Naka, G. Torikai, R. Hino, and F. Yoshida, The Effects of Temperature and Forming Speed on the Forming Limit Diagram for Type 5083 Aluminum-Magnesium Alloy Sheet, J. Mater. Process. Technol., 2001, 113(1–3), p 648–653.10.1016/S0924-0136(01)00650-1 ↗
17. R. Gu, Q. Liu, S. Chen, W. Wang, and X. Wei, Study on High-Temperature Mechanical Properties and Forming Limit Diagram of 7075 Aluminum Alloy Sheet in Hot Stamping, J. Mater. Eng. Perform., 2019, 28, p 7259–7272.10.1007/s11665-019-04436-x ↗
18. S. Basak and S.K. Panda, “Application of Barlat YLD-96 Yield Criterion for Predicting Formability of Pre-Strained Dual Phase Steel Sheets,” International Manufacturing Science and Engineering Conference, 2016, p V001T02A063.10.1115/MSEC2016-8753 ↗
19. S. Basak and S.K. Panda, Necking and Fracture Limit Analyses of Different Pre-Strained Sheet Materials in Polar Effective Plastic Strain Locus Using Yld 2000–2d Yield Model, J. Mater. Process. Technol., 2019, 267, p 289–307.10.1016/j.jmatprotec.2018.10.004 ↗
20. S.S. Panicker and S.K. Panda, Formability Analysis of AA5754 Alloy at Warm Condition: Appraisal of Strain Rate Sensitive Index, Mater. Today Proc., 2015, 2(4–5), p 1996–2004.10.1016/j.matpr.2015.07.169 ↗
21. T.B. Stoughton, General Forming Limit Criterion for Sheet Metal Forming, Int. J. Mech. Sci., 2000, 42(1), p 1–27.10.1016/S0020-7403(98)00113-1 ↗
22. T.B. Stoughton and J.W. Yoon, Path Independent Forming Limits in Strain and Stress Spaces, Int. J. Solids Struct., 2012, 49(25), p 3616–3625. https://doi.org/10.1016/j.ijsolstr.2012.08.00410.1016/j.ijsolstr.2012.08.004 ↗
23. S. Basak and S.K. Panda, Failure Strains of Anisotropic Thin Sheet Metals: Experimental Evaluation and Theoretical Prediction, Int. J. Mech. Sci., 2019, 151, p 356–374.10.1016/j.ijmecsci.2018.10.065 ↗
24. K. Bandyopadhyay, S. Basak, and M.G. Lee, Influence of Anisotropy Evolution on Strain Path Independent Failure Limits, IOP Conf Ser. Mater. Sci. Eng., 2020, 967(1), p 012006.10.1088/1757-899X/967/1/012006 ↗
25. D.R. Behera, B.S. Katiyar, S.K. Panda, S.K. Kar, and S.V.S.N. Murty, Forming Limit and Failure Characterization of As-Received and Bi-Axial Prestrained Cu-Cr-Zr-Ti Alloy Sheets in the Context of Multistage Forming, Mater Charact, 2024, 214, p 114104.10.1016/j.matchar.2024.114104 ↗
26. K. Wang, J.E. Carsley, L. Zhang, T.B. Stoughton, J. Li, and B.E. Carlson, Forming Limits of an Age Hardenable Aluminum Sheet after Pre-Straining and Annealing, Int. J. Mech. Sci., 2014, 82, p 13–24. https://doi.org/10.1016/j.ijmecsci.2014.02.02510.1016/j.ijmecsci.2014.02.025 ↗
27. S. Basak and S.K. Panda, Use of Uncoupled Ductile Damage Models for Fracture Forming Limit Prediction during Two-Stage Forming of Aluminum Sheet Material, J. Manuf. Process., 2023, 97, p 185–199.10.1016/j.jmapro.2023.04.042 ↗
28. N. Kotkunde, G. Krishna, S.K. Shenoy, A.K. Gupta, and S.K. Singh, Experimental and Theoretical Investigation of Forming Limit Diagram for Ti-6Al-4 V Alloy at Warm Condition, Int. J. Mater. Form., 2017, 10(2), p 255–266.10.1007/s12289-015-1274-3 ↗
29. J. Min, J. Lin, J. Li, and W. Bao, Investigation on Hot Forming Limits of High Strength Steel 22MnB5, Comput. Mater. Sci., 2010, 49(2), p 326–332.10.1016/j.commatsci.2010.05.018 ↗
30. R. Chiba, Reliability Analysis of Forming Limits of Anisotropic Metal Sheets with Uncertain Material Properties, Comput. Mater. Sci., 2013, 69, p 113–120.10.1016/j.commatsci.2012.11.044 ↗
31. S. Kurra, N.H. Rahman, S.P. Regalla, and A.K. Gupta, Modeling and Optimization of Surface Roughness in Single Point Incremental Forming Process, J. Mater. Res. Technol., 2015, 4(3), p 304–313.10.1016/j.jmrt.2015.01.003 ↗
32. A. Forcellese, F. Gabrielli, and M. Simoncini, Prediction of Flow Curves and Forming Limit Curves of Mg Alloy Thin Sheets Using ANN-Based Models, Comput. Mater. Sci., 2011, 50(11), p 3184–3197.10.1016/j.commatsci.2011.05.048 ↗
33. I. Czinege and D. Harangozó, Application of Artificial Neural Networks for Characterisation of Formability Properties of Sheet Metals, Int. J. Light. Mater. Manuf., 2024, 7(1), p 37–44.
34. J. Kim, A.S. Ebrahim, B.L. Kinsey, and J. Ha, Identification of Yld 2000–2d Anisotropic Yield Function Parameters from Single Hole Expansion Test Using Machine Learning, CIRP Ann., 2024, 73(1), p 233–236.10.1016/j.cirp.2024.04.026 ↗
35. J. Cao, M. Bambach, M. Merklein, M. Mozaffar, and T. Xue, Artificial Intelligence in Metal Forming, CIRP Ann., 2024, 73(2), p 561–587.10.1016/j.cirp.2024.04.102 ↗
36. D. Möllensiep, J. Schäfer, F. Pasch, and B. Kuhlenkötter, Cluster Analysis for Systematic Database Extension to Improve Machine Learning Performance in Double-Sided Incremental Sheet Forming, Int. J. Adv. Manuf. Technol., 2024, 133(9–10), p 4301–4315.10.1007/s00170-024-14014-8 ↗
37. Q. Zhang and Y. Wang, Research on Mechanical Property Prediction of Hot Rolled Steel Based on Lightweight Multi-Branch Convolutional Neural Network, Mater. Today Commun., 2023, 37, p 107445.10.1016/j.mtcomm.2023.107445 ↗
38. Y. Li and S. Li, Deep Learning Based Phase Transformation Model for the Prediction of Microstructure and Mechanical Properties of Hot-Stamped Parts, Int. J. Mech. Sci., 2022, 220, p 107134. https://doi.org/10.1016/j.ijmecsci.2022.10713410.1016/j.ijmecsci.2022.107134 ↗
39. C. Jaremenko, N. Ravikumar, E. Affronti, M. Merklein, and A. Maier, Determination of Forming Limits in Sheet Metal Forming Using Deep Learning, Materials (Basel), 2019, 12(7), p 1–17.10.3390/ma12071051 ↗
40. M. Zhang, D. Chen, H. Liu, Y. Zhang, H. Song, Y. Xu, and S. Zhang, Research on Hot Deformation Behavior of Cu-Ti Alloy Based on Machine Learning Algorithms and Microalloying, Mater. Today Commun., 2024, 39, p 108783.10.1016/j.mtcomm.2024.108783 ↗
41. S. Liu, Y. Xia, Z. Shi, H. Yu, Z. Li, and J. Lin, Deep Learning in Sheet Metal Bending with a Novel Theory-Guided Deep Neural Network, IEEE/CAA J. Autom. Sin., 2021, 8(3), p 565–581.10.1109/JAS.2021.1003871 ↗
42. A.M. Chheda, L. Nazro, F.G. Sen, and V. Hegadekatte, Prediction of Forming Limit Diagrams Using Machine Learning, IOP Conference Series: Materials Science and Engineering, 2019, 651, p 12107.10.1088/1757-899X/651/1/012107 ↗
43. K. Elangovan, C.S. Narayanan, and R. Narayanasamy, Modelling of Forming Limit Diagram of Perforated Commercial Pure Aluminium Sheets Using Artificial Neural Network, Comput. Mater. Sci., 2010, 47(4), p 1072–1078.10.1016/j.commatsci.2009.12.016 ↗
44. S. Kannadasan, A.S. Kumar, C. Pandivelan, and C.S. Narayanan, Modelling the Forming Limit Diagram for Aluminium Alloy Sheets Using ANN and ANFIS, Appl. Math. Inf. Sci, 2017, 11, p 1435–1442.10.18576/amis/110521 ↗
45. M. Mohamed, S. Elatriby, Z. Shi, and J.G. Lin, Prediction of Forming Limit Diagram for AA5754 Using Artificial Neural Network Modelling, Key Eng. Mater., 2016, 716, p 770–778.10.4028/www.scientific.net/KEM.716.770 ↗
46. N. Kotkunde, A.D. Deole, and A.K. Gupta, Prediction of Forming Limit Diagram for Ti-6Al-4V Alloy Using Artificial Neural Network, Procedia Mater. Sci., 2014, 6, p 341–346.10.1016/j.mspro.2014.07.043 ↗
47. A. Derogar and F. Djavanroodi, Artificial Neural Network Modeling of Forming Limit Diagram, Mater. Manuf. Process., 2011, 26(11), p 1415–1422.10.1080/10426914.2010.544818 ↗
48. J.L. Ferreira, L.P. Moreira, and M.C. Cardoso, Analysis of the Pre-Straining Effects on the Limit Strains of Interstitial-Free Steel: Experiments and Elasto-Plastic Modeling of Strain and Stress-Based Forming Limit Curves, J. Brazilian Soc. Mech. Sci. Eng., 2021, 43(2), p 1–28. https://doi.org/10.1007/s40430-020-02778-510.1007/s40430-020-02778-5 ↗
49. K. Seshacharyulu, G. Mahalle, N. Kotkunde, S.K. Singh, and B. Balu Naik, High Temperatures Deformation and Formability Behavior of DP590 Steel: Mechanical Characterization and Modeling, J. Brazilian Soc. Mech. Sci. Eng, 2021, 43(10), p 1–23. https://doi.org/10.1007/s40430-021-03196-x10.1007/s40430-021-03196-x ↗
50. F.K. Chen and T. Bin Huang, Formability of Stamping Magnesium-Alloy AZ31 Sheets, J. Mater. Process. Technol., 2003, 142(3), p 643–647.10.1016/S0924-0136(03)00684-8 ↗
51. F. Zhalehfar, S.J. Hosseinipour, S. Nourouzi, and A.H. Gorji, Effect of Equal Biaxial Pre-Strain on Forming Limit Diagram of AA5083, AIP Conf. Proc., 2010, 1315, p 353–358.
52. F. Zhalehfar, R. Hashemi, and S.J. Hosseinipour, Experimental and Theoretical Investigation of Strain Path Change Effect on Forming Limit Diagram of AA5083, Int. J. Adv. Manuf. Technol., 2015, 76(5–8), p 1343–1352.10.1007/s00170-014-6340-3 ↗
53. K.K. Saxena, K. Drotleff, and J. Mukhopadhyay, Elevated Temperature Forming Limit Strain Diagrams of Automotive Alloys Al6014-T4 and DP600: A Case Study, J. Strain Anal. Eng. Des., 2016, 51(6), p 459–470.10.1177/0309324716651028 ↗
54. A. Badrish, A. Morchhale, N. Kotkunde, and S.K. Singh, Influence of Material Modeling on Warm Forming Behavior of Nickel Based Super Alloy, Int. J. Mater. Form., 2020, 13(3), p 445–465.10.1007/s12289-020-01548-x ↗
55. W. Yuan, M. Wan, X. Wu, B. Ma, X. Lu, and B. Yang, Influence of Uniaxial Tensile Pre-Strain on Forming Limit Curve by Using Biaxial Tensile Test, Chinese J. Aeronaut., 2020, 33(8), p 2268–2280.10.1016/j.cja.2019.08.019 ↗
56. X.Y. Yang, L.H. Lang, K.N. Liu, and C. Guo, Modified MK Model Combined with Ductile Fracture Criterion and Its Application in Warm Hydroforming, Trans. Nonferrous Met. Soc. China, 2015, 25(10), p 3389–3398. https://doi.org/10.1016/S1003-6326(15)63974-710.1016/S1003-6326(15)63974-7 ↗
57. B. Dharavath, A. Morchhale, S.K. Singh, N. Kotkunde, and M.T. Naik, Experimental Determination and Theoretical Prediction of Limiting Strains for ASS 316L at Hot Forming Conditions, J. Mater. Eng. Perform., 2020, 29(7), p 4766–4778. https://doi.org/10.1007/s11665-020-04968-710.1007/s11665-020-04968-7 ↗
58. S.M. Hussaini, G. Krishna, A.K. Gupta, and S.K. Singh, Development of Experimental and Theoretical Forming Limit Diagrams for Warm Forming of Austenitic Stainless Steel 316, J. Manuf. Process., 2015, 18, p 151–158. https://doi.org/10.1016/j.jmapro.2015.03.00510.1016/j.jmapro.2015.03.005 ↗
59. R.R. Goud, K.E. Prasad, and S.K. Singh, Formability Limit Diagrams of Extra-Deep-Drawing Steel at Elevated Temperatures, Procedia Mater. Sci., 2014, 6, p 123–128. https://doi.org/10.1016/j.mspro.2014.07.01410.1016/j.mspro.2014.07.014 ↗
60. M. Shakeri, A. Sadough, and B.M. Dariani, Effect of Pre-Straining and Grain Size on the Limit Strains in Sheet Metal Forming, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 2000, 214(9), p 821–827.10.1243/0954405001517892 ↗
61. G.S. Huang, H. Zhang, X.Y. Gao, B. Song, and L. Zhang, Forming Limit of Textured AZ31B Magnesium Alloy Sheet at Different Temperatures, Trans. Nonferrous Met. Soc., 2011, 21(4), p 836–843. https://doi.org/10.1016/S1003-6326(11)60790-510.1016/S1003-6326(11)60790-5 ↗
62. W. Li, G. Zhao, X. Ma, and J. Gao, Study on Forming Limit Diagrams of AZ31B Alloy Sheet at Different Temperatures, Mater. Manuf. Process., 2013, 28(3), p 306–311.10.1080/10426914.2012.689453 ↗
63. E. Hsu, J.E. Carsley, and R. Verma, Development of Forming Limit Diagrams of Aluminum and Magnesium Sheet Alloys at Elevated Temperatures, J. Mater. Eng. Perform., 2008, 17(3), p 288–296.10.1007/s11665-007-9196-y ↗
64. A. Jaimin, N. Kotkunde, A. Morchhale, A.R. Anand, A. Sadhukhan, and S.K. Singh, Temperature and Strain Rate Dependent Anisotropic Plastic Deformation Behavior of AZ31B Mg Alloy, Indian J. Eng. Mater. Sci., 2022, 29(6), p 723–729.
65. S.S. Panicker, and S.K. Panda, Investigations into Improvement in Formability of AA5754 and AA6082 Sheets at Elevated Temperatures, J. Mater. Eng. Perform., 2019, 28, p 2967–2982.10.1007/s11665-019-04030-1 ↗
66. K.S. Prasad, S.K. Panda, S.K. Kar, S. Murty, and S.C. Sharma, Constitutive Modelling and Formability Prediction of Inconel-718 Sheet at Elevated Temperatures for Space Applications, Adv. Mater. Process. Technol., 2022, 8(1), p 518–541.
67. T. Kamal and A. Upadhyaya, Machine Learning Aided Prediction of Sintering Induced Swelling in Powder Metallurgical Cu-Sn Alloys, Comput. Mater. Sci., 2024, 235, p 112827.10.1016/j.commatsci.2024.112827 ↗
68. P. Kumar and H. Singh, Experimental Analysis of Tool Geometry and Tool Rotation in SPIF Process on AA7075-O Alloy Using ML and ANN Approach, Int. J. Interact. Des. Manuf., 2023 https://doi.org/10.1007/s12008-023-01535-x10.1007/s12008-023-01535-x ↗
69. M. Murugesan, J.-H. Yu, K.-S. Jung, S.-M. Cho, K.S. Bhandari, W. Chung, and C.-W. Lee, Supervised Machine Learning Approach for Modeling Hot Deformation Behavior of Medium Carbon Steel, steel Res, Int., 2023, 94(2), p 2200188.
70. F.P. Finamor, M.A. Wolff, and V.S. Lage, Prediction of Forming Limit Diagrams from Tensile Tests of Automotive Grade Steels by a Machine Learning Approach, IOP Conference Series: Materials Science and Engineering, 2021, 1157, p 12080.10.1088/1757-899X/1157/1/012080 ↗
71. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 2013.
72. M.S. Niketh, N. Radhika, A.A. Adediran, and T.-C. Jen, Enhancing High-Entropy Alloy Performance: Predictive Modelling of Wear Rates with Machine Learning, Results Eng., 2024, 23, p 102387.10.1016/j.rineng.2024.102387 ↗
73. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, Scikit-Learn: Machine Learning in Python, J. Mach. Learn. Res., 2011, 12, p 2825–2830.
74. F. Barlat, J.C. Brem, J.W. Yoon, K. Chung, R.E. Dick, D.J. Lege, F. Pourboghrat, S.-H. Choi, and E. Chu, Plane Stress Yield Function for Aluminum Alloy Sheets—Part 1: Theory, Int. J. Plast., 2003, 19(9), p 1297–1319.10.1016/S0749-6419(02)00019-0 ↗
75. T.B. Stoughton and J.W. Yoon, Sheet Metal Formability Analysis for Anisotropic Materials under Non-Proportional Loading, Int. J. Mech. Sci., 2005, 47(12), p 1972–2002.10.1016/j.ijmecsci.2005.06.005 ↗
76. S.S. Panicker and S. Kumar Panda, Improvement in Material Flow during Nonisothermal Warm Deep Drawing of Nonheat Treatable Aluminum Alloy Sheets, J. Manuf. Sci. Eng., 2017, 139(3), p 31013.10.1115/1.4034594 ↗
77. M.A. Iadicola, T. Foecke, and S.W. Banovic, Experimental Observations of Evolving Yield Loci in Biaxially Strained AA5754-O, Int. J. Plast., 2008, 24(11), p 2084–2101.10.1016/j.ijplas.2008.03.003 ↗
78. A. Samad, S. Arif, S. Ansari, M. Muaz, M. Mohsin, A.U. Khan, M.E. Khan, A.H. Bashiri, W. Zakri, and W. Ali, Machine Learning Enabled Prediction of Tribological Properties of Cu-TiC-GNP Nanocomposites Synthesized by Electric Resistance Sintering: A Comparison with RSM, J. Mater. Res. Technol., 2024, 28, p 2290–2312.10.1016/j.jmrt.2023.12.132 ↗
79. S.M. Najm and I. Paniti, Investigation and Machine Learning-Based Prediction of Parametric Effects of Single Point Incremental Forming on Pillow Effect and Wall Profile of AlMn1Mg1 Aluminum Alloy Sheets, J. Intell. Manuf., 2023, 34(1), p 331–367.10.1007/s10845-022-02026-8 ↗
80. S.M. Najm, T. Trzepieciński, and M. Kowalik, Modelling and Parameter Identification of Coefficient of Friction for Deep-Drawing Quality Steel Sheets Using the CatBoost Machine Learning Algorithm and Neural Networks, Int. J. Adv. Manuf. Technol., 2023, 124(7), p 2229–2259.10.1007/s00170-022-10544-1 ↗
81. A.E. Marques, T.G. Parreira, A.F.G. Pereira, B.M. Ribeiro, and P.A. Prates, Machine Learning Application to the Identification of Sheet Metal Constitutive Model Parameters, Mach. Learn. Comput. Sci. Eng., 2025, 1(1), p 10.10.1007/s44379-024-00006-8 ↗
82. H. Van Minh, R. Sowerby, and J.L. Duncan, Variability of Forming Limit Curves, Int. J. Mech. Sci., 1974, 16(1), p 31–44.10.1016/0020-7403(74)90031-9 ↗