ANN-enhanced determination and numerical model integration of activation energy and Zener–Hollomon parameter to evaluate microstructure evolution of AA6082 wheel forging

Saputro, Imang Eko; Lin, Chun-Nan; Mardiono, Intan; Chen, Hsuan-Fan; Chen, Junwei; Ho, Marlon; Fuh, Yiin-Kuen

doi:10.1007/s43452-024-01073-7

Artykuł - szczegóły

Tytuł artykułu

ANN-enhanced determination and numerical model integration of activation energy and Zener–Hollomon parameter to evaluate microstructure evolution of AA6082 wheel forging

Autorzy

Saputro Imang Eko , Lin Chun-Nan , Mardiono Intan , Chen Hsuan-Fan , Chen Junwei , Ho Marlon , Fuh Yiin-Kuen

Wybrane pełne teksty z tego czasopisma

http://www.springer.com/journal/43452

Identyfikatory

DOI

10.1007/s43452-024-01073-7

Warianty tytułu

Języki publikacji

Abstrakty

This study presents the integration of two Arrhenius-constitutive model parameters, activation energy (Q) and the Zener–Hollomon parameter (Z), into a numerical model to evaluate their correlation with the microstructural evolution of AA6082 wheel forging. Isothermal tests powered by a Gleeble machine were conducted to establish the constitutive model of AA6082 material, with deformation temperatures and strain rates varying between 350–560 °C and 0.05–15 s⁻1, respectively. Two types of Arrhenius methods were employed: strain-compensated Arrhenius and artificial neural network (ANN)-enhanced Arrhenius. The key difference between the two methods is that the former ignores the effects of deformation temperature and strain rate when determining the activation energy (Q) value, while the latter considers these factors. Integrating activation energy and Zener–Hollomon parameters into a numerical model by directly inputting the mathematical equation from the strain-compensated Arrhenius method resulted in significant overfitting at certain nodes and elements. To address this issue, a new approach using trilinear interpolation and behavior-based clamping methods on Q values generated by the ANN–Arrhenius method proved effective. Additionally, the ANN–Arrhenius method demonstrated superior accuracy, reducing the prediction’s average absolute relative error (AARE) from 3.14% (strain-compensated Arrhenius method) to 1.10%. A comparative study of the distribution of Q and Z values in numerical model simulations, alongside average grain size and shape examined with an optical microscope, revealed that the Q and Z parameters are beneficial for predicting grain characteristics in final workpieces. This study aims to bridge the gap in implementing activation energy and Zener–Hollomon parameters in more realistic forging scenarios and with more complex workpiece designs.

Słowa kluczowe

Arrhenius constitutive model activation energy Zener-Hollomon parameter wheel forging ANN trilinear interpolation

energia aktywacji interpolacja trójliniowa parametr Zenera-Hollomona

Wydawca

Springer
Wrocław University of Science and Technology

Czasopismo

Archives of Civil and Mechanical Engineering

Rocznik

2025

Tom

Vol. 25, no. 1

Strony

art. no. e9, 2025

Opis fizyczny

Bibliogr. 47 poz., rys., wykr

Twórcy

autor

Saputro Imang Eko

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

autor

Lin Chun-Nan

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

autor

Mardiono Intan

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

autor

Chen Hsuan-Fan

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

autor

Chen Junwei

SuperAlloy Industrial Co., Ltd, Yunlin County, No. 80, Nanshang Road, Section 3, Yunke Road, Douliu City 64064, Taiwan

autor

Ho Marlon

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

autor

Fuh Yiin-Kuen

mikefuh@ncu.edu.tw

Department of Mechanical Engineering, Zhongli District, National Central University, No. 300, Zhongda Road, Taoyuan City 320317, Taiwan

https://orcid.org/0000-0001-5322-0718

Bibliografia

1. Global Market Insight. https:// www. gmins ights. com/ industry-analysis/automotive-wheel-market (accessed March 13th, 2024).
2. Czerwinski F. Current trends in automotive lightweighting strate-gies and materials. Materials. 2021;14(21):6631.
3. Graf A. Aluminum alloys for lightweight automotive structures. In: Materials, design and manufacturing for Lightweight Vehicles. Elsevier; 2021. p. 97–123.
4. Long R, Boettcher E, Crawford D. Current and future uses of aluminum in the automotive industry. JOM. 2017;69(12):2635–9.
5. Dragatogiannis DA, Kollaros D, Karakizis P, Pantelis D, Lin J, Charitidis C. Friction stir welding between 6082 and 7075 alu-minum alloys thermal treated for automotive applications. MatPerform Charact. 2019;8(4):571–89.
6. Liu J, et al. Hot stamping of AA6082 tailor welded blanks for automotive applications. Procedia Eng. 2017;207:729–34.
7. Xu Z, Ma H, Zhao N, Hu Z. Investigation on compressive formability and microstructure evolution of 6082–T6 aluminum alloy. Metals. 2020;10(4):469.
8. Luo R, et al. Characterization of hot workability of IN617B alloy using activation energy, Zener-Hollomon parameter and hot processing maps. J Market Res. 2023;26:5141–50.
9. Long J, Xia Q, Xiao G, Qin Y, Yuan S. Flow characterization of magnesium alloy ZK61 during hot deformation with improved constitutive equations and using activation energy maps. Int JMech Sci. 2021. https://doi.org/10.1016/j.ijmecsci.2020.106069.
10. Malik A, Long J, Li C. Achieving low activation energy and dou-ble-peak texture after hot deformation of Mg–Al–Zn–Ca–Mn–Zr alloy and enabling the strength and ductility. J Market Res.2023;25:6812–28.
11. Zyguła K, et al. The analysis of hot deformation behavior of powder metallurgy Ti-10V-2Fe-3Al alloy using activation energy and Zener-Hollomon parameter. Procedia Manufact. 2020;50:546–51.
12. H. Li, L. Fan, M. Zhou, Y. Zhou, K. Jiang, and Y. Chen, "Hot Compression Deformation and Activation Energy of Nanohybrid-Reinforced AZ80 Magnesium Matrix Composite," Metals, vol. 10, no. 1, p. 119, 2020. [Online]. Available: https://www.mdpi.com/2075-4701/10/1/119.
13. Long J, et al. Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model. J Magnes Alloy.2024. https://doi.org/10.1016/j.jma.2024.01.011.
14. Ling K, Mo W, Deng P, Chen J, Luo B, Bai Z. Hot deformation behavior and dynamic softening mechanisms of hot-extruded Al-Cu-Mg-Ag-Mn-Zr-Ti alloy. Mat Today Commun.2023;34:105300.
15. Quan G-Z, Zhang L, Wang X. Correspondence between micro-structural evolution mechanisms and hot processing parameters for Ti-13Nb-13Zr biomedical alloy in comprehensive processing maps. J Alloy Compd. 2017;698:178–93.
16. Song Y, Cai Z, Zhao G, Li Y, Li H, Zhang M. Hot deformation behavior of 309L stainless steel. Mat Today Commun.2023;36:106877.
17. Chakraborty D, Shankar G, Prajapati A, Reddy BCO, Kumar A. Hot deformation characteristics and microstructural evolution of Ti-900 alloy. Mater Lett. 2024;355:135546.
18. Long S, et al. Recrystallization behavior and kinetic analysis of an Al-Cu-Li alloy during hot deformation and subsequent heat treatment. Mater Charact. 2024;208:113640.
19. Yu Y, Pan Q, Wang W, Huang Z, Xiang S, Liu B. Dynamic softening mechanisms and Zener-Hollomon parameter of Al–Mg–Si–Ce–B alloy during hot deformation. J Mat Res Technol. 2021.https://doi.org/10.1016/j.jmrt.2021.11.081.
20. Malik A, Nazeer F, Al-Sehemi AG. Constitutive description, processing maps, and unexplored deformation mechanisms of pure Mg and Mg-6Al (wt%) alloy under hot compression. J Alloy Compd. 2024;970:172629.
21. Montaña Y, Idoyaga Z, Gutiérrez I, Iza-Mendia A. Pearlite sphe-roidisation and microstructure refinement through heavy warm deformation of hot rolled 55VNb microalloyed steel. Metall and Mater Trans A. 2022;53(7):2586–99.
22. Liao W-H, Tsai C-W, Tzeng Y-C, Wang W-R, Chen C-S, Yeh J-W. Exploring hot deformation behavior of equimolar cocrfeni high-entropy alloy through constitutive equations and microstructure characterization. Mater Charact. 2023;205:113234.
23. Nan X, et al. Microstructural evolution and mechanical properties of cooling medium assistant friction stir processed AZ31B Mg alloy. Transact Nonferrous Metal Soci China.2023;33(6):1729–41.
24. Tan G, et al. Effect of Zener-Hollomon parameter on microstructure evolution of a HEXed PM nickel-based superalloy. J Alloys Compound. 2021. https:// doi. org/ 10. 1016/j. jallcom.2021.159889.
25. Xu D, et al. Microstructure and hot deformation behavior of the Cu-Sn-Ni-Zn-Ti (-Y) alloy. Mater Charact. 2023;196:112559.
26. Song T, Xu S, Li Y, Ding H. Hot deformation and dynamic recrystallization behavior of a Cu-9Ni-6Sn-0.04Cr alloy. Mat TodayCommun. 2023. https://doi.org/10.1016/j.mtcomm.2023.105828.
27. Yang P, Liu C, Guo Q, Liu Y. Variation of activation energy determined by a modified Arrhenius approach: Roles of dynamic recrystallization on the hot deformation of Ni-based superalloy. J Mat Sci Technol. 2021;72:162–71. https://doi.org/10.1016/j.jmst.2020.09.024.
28. Jain R, Umre P, Sabat RK, Kumar V, Samal S. Constitutive and artificial neural network modeling to predict hot deformation behavior of CoFeMnNiTi eutectic high-entropy alloy. J Mat Eng Perform. 2022;31(10):8124–35. https:// doi. org/ 10. 1007/s11665-022-06829-x.
29. X. Jia, K. Hao, Z. Luo, and Z. Fan, "Plastic Deformation Behavior of Metal Materials: A Review of Constitutive Models," Metals, vol. 12, no. 12, p. 2077, 2022. [Online]. Available: https://www.mdpi.com/2075-4701/12/12/2077.
30. Savaedi Z, Motallebi R, Mirzadeh H. A review of hot deformation behavior and constitutive models to predict flow stress of high-entropy alloys. J Alloy Compound. 2022;903:163964. https://doi.org/10.1016/j.jallcom.2022.163964.
31. Fuh Y-K, et al. Preform design with increased materials utilization and processing map analysis for aluminum hot forging process. JManuf Process. 2023;90:14–27.
32. Lin C-N, et al. Optimization of hot deformation processing parameters for as-extruded 7005 alloys through the integration of 3D processing maps and FEM numerical simulation. J Alloy Compd.2023;948:169804.
33. Lin C-N, et al. Microstructure evolution and Zener-Hollomon parameter analysis as-extruded 7005 aluminum alloy during hot deformation. Mat Today Commun. 2023;37:107604.
34. Wojtaszek M, et al. Application of processing maps and numerical modelling for identification of parameters and limitations of hot forging process of 80MnSi8–6 steel. Arch Civil Mech Eng.2023;23(4):240. https://doi.org/10.1007/s43452-023-00783-8.
35. Chen M-S, Lin Y, Ma X-S. The kinetics of dynamic recrystallization of 42CrMo steel. Mater Sci Eng, A. 2012;556:260–6.
36. Chen M-S, Yuan W-Q, Li H-B, Zou Z-H. New insights on the relationship between flow stress softening and dynamic recrystallization behavior of magnesium alloy AZ31B. Mater Charact.2019;147:173–83.
37. Ding S, Shi Q, Chen G. Flow stress of 6061 aluminum alloy attypical temperatures during friction stir welding based on hot compression tests. Metals. 2021;11(5):804.
38. Ji H, Cai Z, Pei W, Huang X, Lu Y. DRX behavior and microstructure evolution of 33Cr23Ni8Mn3N: Experiment and finite element simulation. J Market Res. 2020;9(3):4340–55.
39. Wang H, Qin G, Li C. A modified Arrhenius constitutive model of 2219-O aluminum alloy based on hot compression with simulation verification. J Market Res. 2022;19:3302–20.
40. Wang Y, Zhao G, Xu X, Chen X, Zhang C. Constitutive modeling, processing map establishment and microstructure analysis of spray deposited Al-Cu-Li alloy 2195. J Alloy Compd. 2019;779:735–51.
41. Zhang Y-Q, Quan G-Z, Lei S, Zhao J, Xiong W. Description of dynamic recrystallization behaviors and grain evolution mechanisms during the hot forming process for SAE 5137H steel. Materials. 2022;15(16):5593.
42. Wang Y, Peng J, Zhong L, Pan F. Modeling and application of constitutive model considering the compensation of strain during hot deformation. J Alloy Compd. 2016;681:455–70.
43. Hu L, et al. Study on hot deformation behavior of homogenized Mg-8.5 Gd-4.5 Y-0.8 Zn-0.4 Zr alloy using a combination of strain-compensated Arrhenius constitutive model and finite element simulation method. J Magnes Alloy. 2023;11(3):1016–28.
44. Chen W, Li S, Bhandari KS, Aziz S, Chen X, Jung DW. Genetic optimized Al–Mg alloy constitutive modeling and activation energy analysis. Int J Mech Sci. 2023;244:108077. https://doi.org/10.1016/j.ijmecsci.2022.108077.
45. Deb S, Muraleedharan A, Immanuel RJ, Panigrahi SK, Racineux G, Marya S. Establishing flow stress behaviour of Ti-6Al-4Valloy and development of constitutive models using Johnson-Cook method and Artificial Neural Network for quasi-static and dynamic loading. Theor Appl Fract Mech. 2022;119:103338.https://doi.org/10.1016/j.tafmec.2022.103338.
46. Petrov P, et al. Finite-element modelling of forging with torsion: Investigation of heat effect. Procedia Manuf. 2020;47:274–81.
47. Łukaszek-Sołek A, Krawczyk J, Śleboda T, Grelowski J. Optimization of the hot forging parameters for 4340 steel by processing maps. J Market Res. 2019;8(3):3281–90.

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-11419a16-708b-456a-9bb5-a593ce81401c