Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Springback (SBP) is a critical phenomenon in metal forming processes, influencing the dimensional accuracy and mechanical integrity of manufactured components. This study investigates the springback behavior of aluminum, copper, and pure iron using a hybrid approach that integrates finite element analysis (FEA) and machine learning (ML). The research evaluates key parameters, including material deformation, peak forming force, stress distribution, and thermal effects, under varying thicknesses and punch radii. Results reveal that aluminum exhibits the highest springback (6.2%) due to its ductility, followed by copper (4.0%) and pure iron (2.5%), which demonstrated superior dimensional stability. The forming force requirements were lowest for aluminum (50 kN), moderate for copper (75 kN), and highest for iron (100 kN), reflecting their respective material strengths. Copper recorded the highest temperature rise (350°C), while iron exhibited the greatest Von Mises stress (420 MPa), emphasizing its robustness but susceptibility to localized stress. The hybrid FEA-ML model effectively predicted springback angles with high accuracy, optimizing forming parameters and minimizing experimental reliance. These findings highlight the significance of material selection and process optimization in industrial applications, where aluminum is ideal for lightweight structures, iron for strength-critical designs, and copper for high-ductility requirements. This study offers a novel framework for enhancing precision in metal forming processes, with implications for automotive, aerospace, and structural industries. Future research can extend this model to complex geometries and multi-material systems, advancing sustainable and efficient manufacturing technologies
Wydawca
Rocznik
Tom
Strony
185--199
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
autor
- Department of Mechanical Engineering, College of Engineering, University of Babylon, Babylon 51001, Iraq
autor
- Department of Mechanical Engineering, College of Engineering, University of Babylon, Babylon 51001, Iraq
Bibliografia
- 1. Meda, D. P. (2020). Modeling and Experimental Investigation of Springback in Brass Alloy Sheet Metal V-Bending (Master’s thesis, Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)).
- 2. Attar, H. R., Zhu, L., Li, N. (2023, September). Check for updates Deep Learning Enabled ToolCompensation for Addressing Shape Distortion in Sheet Metal Stamping. In Proceedings of the 14th International Conference on the Technology of Plasticity-Current Trends in the Technology of Plasticity: ICTP 2023 4 (p. 48). Springer Nature.
- 3. Bolar, A. L. Automation of a Multi-Stage T-Joint Assembly of Stamped Components and Prediction of Performance Parameters Using Machine Learning Master’s thesis, The Ohio State University) 2023.
- 4. Wang, Z., Xiang, Y., Zhang, S., Liu, X., Ma, J., Tan, J., & Wang, L. Physics-informed springback prediction of 3D aircraft tubes with six-axis free-bending manufacturing. Aerospace Science and Technology 2024; 147, 109022.
- 5. Etim, B., Al-Ghosoun, A., Renno, J., Seaid, M., Mohamed, M. S. Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview. Buildings 2024; 14(11), 3515.
- 6. Xu, J. Machine learning applications for studying the structural behaviour of cold-formed steel columns with web openings. Doctoral dissertation 2022; ResearchSpace, Auckland.
- 7. He, J., Cu, S., Xia, H., Sun, Y., Xiao, W., & Ren, Y. High accuracy roll forming springback prediction model of SVR based on SA-PSO optimization. Journal of Intelligent Manufacturing 2023; 1–17.
- 8. Zeinolabedin-Beygi, A., Naeini, H. M., Talebi-Ghadikolaee, H., Rabiee, A. H., Hajiahmadi, S. Predictive modeling of spring-back in pre-punched sheet roll forming using machine learning. The Journal of Strain Analysis for Engineering Design 2024; 59(7), 463–474.
- 9. Safari, M., Rabiee, A. H., & Joudaki, J. Developing a support vector regression (SVR) model for prediction of main and lateral bending angles in laser tube bending process. Materials 2023; 16(8), 3251.
- 10. Lei, C., Mao, J., Zhang, X., Wang, L., Chen, D. Crack prediction in sheet forming of zirconium alloys used in nuclear fuel assembly by support vector machine method. Energy Reports 2021; 7, 5922–5932.
- 11. Feng, Y., Hong, Z., Gao, Y., Lu, R., Wang, Y., Tan, J. Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region. The International Journal of Advanced Manufacturing Technology 2019; 105, 4265–4278.
- 12. Wang, H., Chen, L., Ye, F., Wang, J. A multi-hierarchical successive optimization method for reduction of spring-back in autoclave forming. Composite Structures 2018; 188, 143–158.
- 13. Cinar, Z., Asmael, M., Zeeshan, Q., & Safaei, B. Effect of springback on A6061 sheet metal bending: a review. Jurnal Kejuruteraan 2021; 33(1), 13–26.
- 14. Yue, Z., Qi, J., Zhao, X., Badreddine, H., Gao, J., Chu, X. Springback prediction of aluminum alloy sheet under changing loading paths with consideration of the influence of kinematic hardening and ductile damage. Metals 2018; 8(11), 950.
- 15. Tseng, A. A., Jen, K. P., Chen, T. C., Kondetimmamhalli, R., & Murty, Y. V. Forming properties and springback evaluation of copper beryllium sheets. Metallurgical and Materials Transactions A, 1995; 26, 2111–2121.
- 16. Pandit, A., Das, S., & Das, S. K. Investigation on Spring-Back Effect of Galvanized Iron Sheet. Reason-A Technical Journal (Formerly Reason-A Technical Magazine) 2020; 81–93.
- 17. Aday, A. J. Analysis of springback behavior in steel and aluminum sheets using FEM. Ann. de Chim. Sci. des Materiaux 2019; 43(2), 95–98.
- 18. Tejyan, S., Kumar, N., Ravi, R. K., Singh, V., & Gangil, B. Analysis of spring back effect for AA6061 alloy sheet using finite element analysis. Materials Today: Proceedings 2024.
- 19. Wang, X. Z., Masood, S. H., Ng, D., & Dawwas, O. A Study of Springback of Sheet Metal Formed Parts Using ANSYS. Advanced Materials Research 2011; 291, 381–384.
- 20. Zhang, Z., Zheng, C., Liu, J., Zhong, Y. Springback research of tubular structure under lateral compression using explicit and implicit FEA method. Journal of Physics: Conference Series 2024; 2820(1), 012061.
- 21. Abdulhasan, M. Q. Design of flexible tool for the evaluation of plate springback Doctoral dissertation, Ministry of Higher Education 2019.
- 22. Joseph, C. D. Experimental measurement and finite element simulation of springback in stamping aluminum alloy sheets for auto-body panel application.
- 23. Prakash, S., Ethier, C. R. Requirements for mesh resolution in 3D computational hemodynamics. J. Biomech. Eng. 2001; 123(2), 134–144.
- 24. Adrian, A. F. Automation and Validation of Big Data Generation via Simulation Pipeline for Flexible Assemblies. Master’s thesis, The Ohio State University 2022.
- 25. Joseph, C. D. Experimental measurement and finite element simulation of springback in stamping aluminum alloy sheets for auto-body panel application.Mississippi State University 2003.
- 26. Dezelak, M., Pahole, I., Ficko, M., Brezocnik, M. Machine learning for the improvement of springback modelling. Advances in Production Engineering & Management 2012; 7(1).
- 27. Jamli, M. R., Farid, N. M. The sustainability of neural network applications within finite element analysis in sheet metal forming: A review. Measure- ment 2019; 138, 446–460.
- 28. Kumar, P. A Study on the Extraction of Geometrical Parameters from Flexible Mechanical Components and Assemblies and Their Impact on Performance: A Machine Learning Approach. Master’s thesis 2024, Arizona State University.
- 29. Abdullah, E., Jalil, A. Critical Spring Back Characteristics in Aluminum, Copper, and Pure Steel: Experimental Analysis 2024.
- 30. Chandrasekaran, P., Manonmani, K. A review on springback effect in sheet metal forming process. In: Int Conf Syst Sci Control Commun Eng Technol 2015; 43-49.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-63fd8123-0b72-4d6c-88d4-1f0a7f7061ce
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