A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

doi:10.1007/s40436-019-00259-0

Advances in Manufacturing ›› 2019, Vol. 7 ›› Issue (2): 238-247.doi: 10.1007/s40436-019-00259-0

A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

Dong-Dong Chen^1,2, Yong-Cheng Lin^1,2, Xiao-Min Chen³

1 School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, People's Republic of China;
2 State Key Laboratory of High Performance Complex Manufacturing, Changsha 410083, People's Republic of China;
3 College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, People's Republic of China

收稿日期:2018-11-15 修回日期:2019-02-21 出版日期:2019-06-25 发布日期:2019-06-19
通讯作者: Yong-Cheng Lin E-mail:yclin@csu.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (Grant No. 51775564), and the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 2016JJ1017).

A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

Dong-Dong Chen^1,2, Yong-Cheng Lin^1,2, Xiao-Min Chen³

1 School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, People's Republic of China;
2 State Key Laboratory of High Performance Complex Manufacturing, Changsha 410083, People's Republic of China;
3 College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, People's Republic of China

Received:2018-11-15 Revised:2019-02-21 Online:2019-06-25 Published:2019-06-19
Contact: Yong-Cheng Lin E-mail:yclin@csu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Grant No. 51775564), and the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 2016JJ1017).

摘要/Abstract

摘要： In this study, a strategy based on the particle swarm optimization (PSO) algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algorithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality criterion can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSO algorithm, which is an intelligent optimal algorithm. This algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00259-0

关键词: Processing parameters, Microstructure, Particle swarm optimization (PSO) algorithm, Superalloy

Abstract: In this study, a strategy based on the particle swarm optimization (PSO) algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algorithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality criterion can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSO algorithm, which is an intelligent optimal algorithm. This algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00259-0

Key words: Processing parameters, Microstructure, Particle swarm optimization (PSO) algorithm, Superalloy

Dong-Dong Chen, Yong-Cheng Lin, Xiao-Min Chen. A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm[J]. Advances in Manufacturing, 2019, 7(2): 238-247.

参考文献

1. Lin YC, Chen XM (2011) A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des 32(4):1733-1759
2. Alabort E, Reed RC, Barba D (2018) Combined modelling and miniaturised characterisation of high-temperature forging in a nickel-based superalloy. Mater Des 160:683-697
3. He DG, Lin YC, Tang Y et al (2019) Influences of solution cooling on microstructures, mechanical properties and hot corrosion resistance of a nickel-based superalloy. Mater Sci Eng A 746:372-383
4. Seret A, Moussa C, Bernacki M et al (2018) On the coupling between recrystallization and precipitation following hot deformation in a γ-γ' nickel-based superalloy. Metall Mater Trans A 49:4199-4213
5. Momeni A, Abbasi SM, Morakabati M et al (2017) A comparative study on the hot working behavior of Inconel 718 and ALLVAC 718 plus. Metall Mater Trans A 48(3):1216-1229
6. He DG, Lin YC, Huang J et al (2018) EBSD study of microstructural evolution in a nickel-base superalloy during twopass hot compressive deformation. Adv Eng Mater 20(7):1800129
7. He DG, Lin YC, Jiang XY et al (2018) Dissolution mechanisms and kinetics of δ phase in an aged Ni-based superalloy in hot deformation process. Mater Des 156:262-271
8. Arun Babu K, Mandal S, Kumar A et al (2016) Characterization of hot deformation behaviour of alloy 617 through kinetic analysis, dynamic material modeling and microstructural studies. Mater Sci Eng A 664:177-187
9. Pradhan SK, Mandal S, Athreya CN et al (2017) Influence of processing parameters on dynamic recrystallization and the associated annealing twin boundary evolution in a nickel base superalloy. Mater Sci Eng A 700:49-58
10. Zhang C, Zhang LW, Shen WF et al (2017) The processing map and microstructure evolution of Ni-Cr-Mo-based C276 superalloy during hot compression. J Alloys Compd 728:1269-1278
11. He DG, Lin YC, Wang LH (2019) Microstructural variations and kinetic behaviors during meta dynamic recrystallization in a nickel base superalloy with pre-precipitated δ phase. Mater Des 165:107584
12. He DG, Lin YC, Wang LH (2019) Influences of pre-precipitated δ phase on microstructures and hot compressive deformation features of a nickel-based superalloy. Vacuum 161:242-250
13. Nakhaie D, Benhangi PH, Fazeli F et al (2012) Controlled forging of a Nb containing microalloyed steel for automotive applications. Metall Mater Trans A 43(13):5209-5217
14. Liu RQ, Kumar A, Chen ZZ et al (2015) A predictive machine learning approach for microstructure optimization and materials design. Sci Rep 5:1-12
15. Malas JC, Frazier WG, Venugopal S et al (1997) Optimization of microstructure development during hot working using control theory. Metall Mater Trans A 28(9):1921-1930
16. Venugopal S, Medina EA, Malas JC et al (1997) Optimization of microstructure during deformation processing using control theory principles. Scr Mater 36(3):347-353
17. Feng JP, Luo ZJ (2000) A method for the optimal control of forging process variables using the finite element method and control theory. J Mater Process Technol 108(1):40-44
18. He XM, Yu ZQ, Lai XM (2009) Robust parameters control methodology of microstructure for heavy forgings based on Taguchi method. Mater Des 30(6):2084-2089
19. Meyer DG, Wadley HNG (1993) Model-based feedback control of deformation processing with microstructure goals. Metall Trans B 24(2):289-300
20. Recker D, Franzke M, Hirt G (2011) Fast models for onlineoptimization during open die forging. CIRP Ann Manuf Technol 60(1):295-298
21. Lin YC, Chen DD, Chen MS et al (2018) A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine. Neural Comput Appl 29(9):585-596
22. Padhi SK, Sahu RK, Mahapatra SS et al (2017) Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy. Adv Manuf 5(3):231-242
23. Sahu PK, Kumari K, Pal S et al (2016) Hybrid fuzzy-greyTaguchi based multi weld quality optimization of Al/Cu dissimilar friction stir welded joints. Adv Manuf 4(3):237-247
24. Quan GZ, Zhang L, An C et al (2018) Multi-variable and biobjective optimization of electric upsetting process for grain refinement and its uniform distribution. Int J Precis Eng Manuf 19(6):859-872
25. Lodh A, Biswas A, Das S (2015) Modelling hot strength behaviour of steel. Ironmak Steelmak 42(4):290-301
26. Chen DD, Lin YC, Zhou Y et al (2017) Dislocation substructures evolution and an adaptive-network-based fuzzy inference system model for constitutive behavior of a Ni-based superalloy during hot deformation. J Alloy Compd 708:938-946
27. He DG, Lin YC, Chen J et al (2018) Microstructural evolution and support vector regression model for an aged Ni-based superalloy during two-stage hot forming with stepped strain rates. Mater Des 154:51-62
28. Lou Y, Ke CX, Li LX (2013) Accurately predicting high temperature flow stress of AZ80 magnesium alloy with particle swarm optimization-based support vector regression. Appl Math Inf Sci 7:1093-1102
29. Irani R, Nasimi R, Shahbazian M (2015) Approximate predictive control of a distillation column using an evolving artificial neural network coupled with a genetic algorithm. Energ Source Part A 37(5):518-535
30. Wang Y, Li B, Weise T et al (2011) Self-adaptive learning based particle swarm optimization. Inf Sci 180:4515-4538
31. Xu G (2013) An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput 219:4560-4569
32. Ren C, An N, Wang J et al (2014) Optimal parameters selection for BP neural network based on particle swarm optimization:A case study of wind speed forecasting. Knowl Based Syst 56:226-239
33. Juang CF, Hsiao CM, Hsu CH (2010) Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization. IEEE Trans Fuzzy Syst 18:14-26
34. Moharam A, El-Hosseini MA, Ali HA (2016) Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers. Appl Soft Comput 38:727-737
35. Lmalghan R, Rao K, ArunKumar S et al (2018) Machining parameters optimization of AA6061 using response surface methodology and particle swarm optimization. Int J Precis Eng Manuf 19(5):695-704
36. Yu Q, Wang KS (2014) A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method. Adv Manuf 2:32-38
37. Lin YC, Chen XM, Wen DX et al (2014) A physically-based constitutive model for a typical nickel-based superalloy. Comput Mater Sci 83:282-289
38. Lin YC, He DG, Chen MS et al (2016) Study of flow softening mechanisms of a nickel-based superalloy with δ phase. Arch Metall Mater 61(3):1537-1546
39. Chen XM, Lin YC, Wen DX et al (2014) Dynamic recrystallization behavior of a typical nickel-based superalloy during hot deformation. Mater Des 57:568-577
40. Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658-3670
41. Yang C, Gao W, Liu N et al (2015) Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight. Appl Soft Comput 29:386-394
42. Eberhart RC, Shi YH (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In:Proceedings of the congress on evolutionary computation, La Jolla, California, July 16-19, pp 84-88
43. Chen MS, Yuan WQ, Li HB et al (2017) Modeling and simulation of dynamic recrystallization behaviors of magnesium alloy AZ31B using cellular automaton method. Comput Mater Sci 136:163-172
44. Chen MS, Yuan WQ, Li HB et al (2019) New insights on the relationship between flow stress softening and dynamic recrystallization behavior of magnesium alloy AZ31B. Mater Charact 147:173-183
45. Yang XM, Guo HZ, Yao ZK et al (2018) Hot deformation behavior and processing parameter optimization of BT25y alloy with an initial equiaxed microstructure using processing map. Rare Metals 37:778-788
46. Najafi SZ, Momeni A, Jafarian HR et al (2017) Recrystallization, precipitation and flow behavior of D3 tool steel under hot working condition. Mater Charact 132:437-447
47. Wen DX, Lin YC, Li XH et al (2018) Hot deformation characteristics and dislocation substructure evolution of a nickel-base alloy considering effects of δ phase. J Alloys Compd 764:1008-1020
48. Lin YC, Wen DX, Chen XM et al (2016) A novel unified dislocation density based model for hot deformation behavior of a nickel-based superalloy under dynamic recrystallization conditions. Appl Phys A 122(9):805
49. Lin YC, He DG, Chen MS et al (2016) EBSD analysis of evolution of dynamic recrystallization grains and δ phase in a nickelbased superalloy during hot compressive deformation. Mater Des 97:13-24
50. Lin YC, Wu XY, Chen XM et al (2015) EBSD study of a hot deformed nickel-based superalloy. J Alloys Compd 640:101-113

[1]	Ben-Kai Li, Qing Miao, Min Li, Xi Zhang, Wen-Feng Ding. An investigation on machined surface quality and tool wear during creep feed grinding of powder metallurgy nickel-based superalloy FGH96 with alumina abrasive wheels[J]. Advances in Manufacturing, 2020, 8(2): 160-176.
[2]	Behrouz Bagheri, Mahmoud Abbasi. Development of AZ91/SiC surface composite by FSP: effect of vibration and process parameters on microstructure and mechanical characteristics[J]. Advances in Manufacturing, 2020, 8(1): 82-96.
[3]	Ze-Xing Su, Chao-Yang Sun, Ming-Wang Fu, Ling-Yun Qian. Physical-based constitutive model considering the microstructure evolution during hot working of AZ80 magnesium alloy[J]. Advances in Manufacturing, 2019, 7(1): 30-41.
[4]	Qin-Xiang Xia, Jin-Chuan Long, Ning-Yuan Zhu, Gang-Feng Xiao. Research on the microstructure evolution of Ni-based superalloy cylindrical parts during hot power spinning[J]. Advances in Manufacturing, 2019, 7(1): 52-63.
[5]	Jie Sun, Cheng Sheng, Ding-Pu Wang, Jing Zhao, Yun-Hu Zhang, Hong-Gang Zhong, Gui Wang, Qi-jie Zhai. Fine equiaxed dendritic structure of a medium carbon steel cast using pulsed magneto-oscillation melt treatment[J]. Advances in Manufacturing, 2018, 6(2): 189-194.
[6]	Jyotirmoy Nandy, Hrushikesh Sarangi, Seshadev Sahoo. Microstructure evolution of Al-Si-10Mg in direct metal laser sintering using phase-field modeling[J]. Advances in Manufacturing, 2018, 6(1): 107-117.
[7]	Jian-Xin Lin, Xu-Ming Liu, Chuan-Wei Cui, Chuan-Yi Bai, Yu-Ming Lu, Feng Fan, Yan-Qun Guo, Zhi-Yong Liu, Chuan-Bing Cai. A review of thickness-induced evolutions of microstructure and superconducting performance of REBa₂Cu₃O_7-δ coated conductor[J]. Advances in Manufacturing, 2017, 5(2): 165-176.
[8]	Antonello Astarita, Mario Coppola, Sergio Esposito, Mariacira Liberini, Leandro Maio, Ilaria Papa, Fabio Scherillo, Antonino Squillace. Experimental characterization of Ti6Al4V T joints welded through linear friction welding technique: microstructure and NDE[J]. Advances in Manufacturing, 2016, 4(4): 305-313.
[9]	Shun-Li Zhao, Jun-Fei Fan, Jie-Yu Zhang, Kuo-Chih Chou, Hai-Rong Le. High speed steel produced by spray forming[J]. Advances in Manufacturing, 2016, 4(2): 115-122.
[10]	Jin-Ke Yu, Hong-Wei Li, Qi-Jie Zhai, Jian-Xun Fu, Zhi-Ping Luo, Hong-Xing Zheng . Crystal structure and formation mechanism of the secondary phase in Heusler Ni-Mn-Sn-Co materials[J]. Advances in Manufacturing, 2014, 2(4): 353-357.
[11]	Min Jiang,Li-Na Chen,Jin He,Guang-Yao Chen,Chong-He Li,Xiong-Gang Lu. Effect of controlled rolling/controlled cooling parameters on microstructure and mechanical properties of the novel pipeline steel[J]. Advances in Manufacturing, 2014, 2(3): 265-274.
[12]	A. G. Kamble R. Venkata Rao. Experimental investigation on the effects of process parameters of GMAW and transient thermal analysis of AISI321 steel[J]. Advances in Manufacturing, 2013, 1(4): 362-377.
[13]	Yao-Hui Lv,Yu-Xin Liu,Fu-Jia Xu,Bin-Shi Xu. Plasma transferred arc forming technology for remanufacture[J]. Advances in Manufacturing, 2013, 1(2): 187-190.

A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

A strategy to control microstructures of a Ni-based superalloy during hot forging based on particle swarm optimization algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics

本文评价