A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

doi:10.1007/s40436-024-00492-2

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 428-446.doi: 10.1007/s40436-024-00492-2

• • 上一篇

A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren

State Key Laboratory of Advanced Special Steels, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, People's Republic of China

收稿日期:2023-11-01 修回日期:2023-12-21 发布日期:2024-09-07
通讯作者: Song-Zhe Xu,E-mail:songzhex@shu.edu.cn E-mail:songzhex@shu.edu.cn
作者简介:Jing Dai is a Ph.D. candidate at the School of Materials Science and Engineering, Shanghai University. His research interests include intelligent manufacturing and investment casting;
Song-Zhe Xu is an assistant professor and master supervisor at Shanghai University. His research areas include high performance computing, machine lear ning and intelligent manufacturing;
Chao-Yue Chen is an associate professor and master supervisor at Shanghai University. His research interests include metal additive manufacturing and superalloys;
Tao Hu is an associate professor and master supervisor. His research interests include computational materials and alloy design;
San-San Shuai is an associate professor and doctoral supervisor. His research interests include metal solidification under magnetic field, and nondestructive testing technology. Wei-Dong Xuan is a professor and doctoral supervisor at Shanghai University. His research interests include metal solidification, superalloys, and investment casting;
Jiang Wang is a professor and doctoral supervisor at Shanghai University. His research interests include magnetic field-controlled metal solidification and additive manufacturing;
Zhong-Ming Ren is a professor and doctoral supervisor at Shanghai University, and he is the Director of the State Key Laboratory of Advanced Special Steels. His research areas include electromagnetic metallurgy and investment casting.
基金资助:
This work was funded by the National Key Research and Development Program of China (Grant No. 2019YFA0705302) and the National Science and Technology Major Project “Aeroengine and Gas Turbine” of China (Grant No. 2017-VII-0008-0102).

A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren

State Key Laboratory of Advanced Special Steels, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, People's Republic of China

Received:2023-11-01 Revised:2023-12-21 Published:2024-09-07
Contact: Song-Zhe Xu,E-mail:songzhex@shu.edu.cn E-mail:songzhex@shu.edu.cn
Supported by:
This work was funded by the National Key Research and Development Program of China (Grant No. 2019YFA0705302) and the National Science and Technology Major Project “Aeroengine and Gas Turbine” of China (Grant No. 2017-VII-0008-0102).

摘要/Abstract

摘要： Wax pattern fabrication in the investment casting of hollow turbine blades directly determines the dimension accuracy of subsequent casting, and therefore significantly affects the quality of final product. In this work, we develop a machine learning-based multi-objective optimization framework for improving dimension accuracy of wax pattern by optimizing its process parameters. We consider two optimization objectives on the dimension of wax pattern, i.e., the surface warpage and core offset. An active learning of Bayesian optimization is employed in data sampling to determine process parameters, and a validated numerical model of injection molding is used to compute objective results of dimension under varied process parameters. The collected dataset is then leveraged to train different machine learning models, and it turns out that the Gaussian process regression model performs best in prediction accuracy, which is then used as the surrogate model in the optimization framework. A genetic algorithm is employed to produce a non-dominated Pareto front using the surrogate model in searching, followed by an entropy weight method to select the most optimal solution from the Pareto front. The optimized set of process parameters is then compared to empirical parameters obtained from previous trial-and-error experiments, and it turns out that the maximum and average warpage results of the optimized solution decrease 26.0% and 20.2%, and the maximum and average errors of wall thickness compared to standard part decrease from 0.22 mm and 0.051 7 mm using empirical parameters to 0.10 mm and 0.035 6 mm using optimized parameters, respectively. This framework is demonstrated capable of addressing the challenge of dimension control arising in the wax pattern production, and it can be reliably deployed in varied types of turbine blades to significantly reduce the manufacturing cost of turbine blades.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00492-2

关键词: Hollow turbine blade, Wax pattern fabrication, Dimension control, Multi-objective optimization, Machine learning, Numerical simulation

Abstract: Wax pattern fabrication in the investment casting of hollow turbine blades directly determines the dimension accuracy of subsequent casting, and therefore significantly affects the quality of final product. In this work, we develop a machine learning-based multi-objective optimization framework for improving dimension accuracy of wax pattern by optimizing its process parameters. We consider two optimization objectives on the dimension of wax pattern, i.e., the surface warpage and core offset. An active learning of Bayesian optimization is employed in data sampling to determine process parameters, and a validated numerical model of injection molding is used to compute objective results of dimension under varied process parameters. The collected dataset is then leveraged to train different machine learning models, and it turns out that the Gaussian process regression model performs best in prediction accuracy, which is then used as the surrogate model in the optimization framework. A genetic algorithm is employed to produce a non-dominated Pareto front using the surrogate model in searching, followed by an entropy weight method to select the most optimal solution from the Pareto front. The optimized set of process parameters is then compared to empirical parameters obtained from previous trial-and-error experiments, and it turns out that the maximum and average warpage results of the optimized solution decrease 26.0% and 20.2%, and the maximum and average errors of wall thickness compared to standard part decrease from 0.22 mm and 0.051 7 mm using empirical parameters to 0.10 mm and 0.035 6 mm using optimized parameters, respectively. This framework is demonstrated capable of addressing the challenge of dimension control arising in the wax pattern production, and it can be reliably deployed in varied types of turbine blades to significantly reduce the manufacturing cost of turbine blades.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00492-2

Key words: Hollow turbine blade, Wax pattern fabrication, Dimension control, Multi-objective optimization, Machine learning, Numerical simulation

Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren. A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing[J]. Advances in Manufacturing, 2024, 12(3): 428-446.

参考文献

1. Zhang X, Bu K (2018) B-spline contour curve approximation and deformation analysis of complex ceramic core. Proc I Mech E Part B J Eng Manuf 233(6):1663-1673
2. Lu ZL, Jiang B, Zhou JP (2013) Review of main manufacturing processes of complex hollow turbine blades. Virtual Phys Prototy 8(2):87-95
3. Petes F, Voigt R, Blair M (1996) Dimensional repeatabililty of investment castings. In:9th world conference on investment casting. San Francisco
4. Jiang S, Zhang D, Wang W et al (2009) Estimation of displacement field for turbine blade profile based on reverse engineering in investment casting. Spec Cast Nonferrous Alloys 29(1):13-15
5. Wiens K, Eng P (2013) Airfoil thickness as a life limiting factor of gas turbine blades. In:20th symposium of the industrial application of gas turbines committee. Banff, Alberta, Canada. pp 1-14
6. Chauhan AS, Pradyumna R (2015) Shrinkage evaluation in a stepped wax pattern-a simulation approach. In:International conference on mechanical engineering design and analysis. Doha, Qatar
7. Yi CC, Yui JL, Shi CT et al (2011) Core deflection in plastics injection molding. Polym-Plast Technol Eng 50(9):863-872
8. Zhang DH, Cheng YY, Jiang RS et al (2018) Deformation simulation of investment casting and die cavity optimization of turbine blade. In:turbine blade investment casting die technology, Springer, Berlin, Heidelberg
9. Cui K, Wang W, Jiang R et al (2019) A wall-thickness compensation strategy for wax pattern of hollow turbine blade. Chin J Aeronaut 32(8):1982-1993
10. Zhang J, Wang S, Zhou H et al (2022) Manufacturable casting parts design with topology optimization of structural assemblies. Proc I Mech E Part B J Eng Manuf 236(4):401-412
11. Mondal NR, Rafiquzzaman M, Siddique MA et al (2020) Optimization of investment casting parameters to improve the mechanical properties by Taguchi method. Am J Mech Ind Eng 5(5):64-70
12. Vladimír K, Pavel N, Antonín Z et al (2022) Requirements for hybrid technology enabling the production of high-precision thin-wall castings. Materials 15(11):3805. https://doi.org/10.3390/ma15113805
13. Vdovin RA, Smelov VG, Sufiiarov VS et al (2018) Designing of the digital casting process for the gas turbine engine blades with a single-crystal structure. IOP Conf Ser Mater Sci Eng 441(1):012058. https://doi.org/10.1088/1757-899X/441/1/012058
14. Torres-Carrillo S, Siller HR, Vila CC et al (2020) Environmental analysis of selective laser melting in the manufacturing of aeronautical turbine blades. J Clean Prod 246(10):119068. https://doi.org/10.1016/j.jclepro.2019.119068
15. Chauhan AS, Anirudh B, Satyanarayana A et al (2020) FEA optimization of injection parameters in ceramic core development for investment casting of a gas turbine blade. Mater Today Proc 26:2190-2199
16. BakharevA FZ, Costa F et al (2004) Prediction of core shift effects using mold filling simulation. ANTEC-Conf Proc 1:621-625
17. Chauhan AS, Walale A, Satyanarayana A et al (2018) Analysis of shrinkage&warpage in ceramic injection molding of HPT vane leading edge core of a gas turbine casting. Mater Today Proc 5(9):19471-19479
18. He B, Wang DH, Li F et al (2014) Simulation study on wax injection for investment casting. Adv Mater Res 834:1575-1579
19. Oktem H, Erzurumlu T, Uzman I et al (2007) Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Mater Des 28(4):1271-1278
20. Cerda-Flores SC, Rojas-Punzo AA, Nápoles-Rivera F et al (2022) Applications of multi-objective optimization to industrial processes:a literature review. Processes 10(1):133. https://doi.org/10.3390/pr10010133
21. Samson C, Murugan K, Abhra R et al (2021) Optimization of process parameters using response surface methodology:a review. Mater Today Proc 37:1301-1304
22. Schmidt J, Marques M, Botti S et al (2019) Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater 5(1):83. https://doi.org/10.1038/s41524-019-0221-0
23. Xu J, Tan W, Li T (2020) Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm. Comput Electr Eng 87:106751. https://doi.org/10.1016/j.compeleceng.2020.106751
24. Shi D, Sun L, Xie Y (2020) Off-design performance prediction of a S-CO₂ turbine based on field reconstruction using deep-learning approach. Appl Sci 10(14):4999. https://doi.org/10.3390/app10144999
25. Jamil TAMT, Roslan SAH, Rasid ZA et al (2021) Experimental and simulation of aerodynamic performance of the small-scale wind turbine blades for usage in Malaysia. IOP Conf Ser Mater Sci Eng 1092(1):012023. https://doi.org/10.1088/1757-899X/1092/1/012023
26. Akolekar HD, Waschkowski F, Zhao Y et al (2021) Transition modeling for low pressure turbines using computational fluid dynamics driven machine learning. Energies 14(15):4680. https://doi.org/10.3390/en14154680
27. Hamedi M, Farzaneh A (2014) Optimization of dimensional deviations in wax patterns for investment casting. J Comput Appl Mech 45(1):23-28
28. Shahane S, Aluru N, Ferreira P et al (2020) Optimization of solidification in die casting using numerical simulations and machine learning. J Manuf Proc 51:130-141
29. Ezhilsabareesh K, Rhee SH, Samad A (2018) Shape optimization of a bidirectional impulse turbine via surrogate models. Eng Appl Comput Fluid Mech 12(1):1-12
30. Herman A, Kubelková I, Vrátn O (2019) New use of instruments for more accurate wax pattern blade segment production. Arch Foundry Eng 19(2):101. https://doi.org/10.24425/afe.2019.127124
31. Zhang XH, Gao HS, Yu KH et al (2018) Crystal orientation effect and multi-fidelity optimization of a solid single crystal superalloy turbine blade. Comp Mater Sci 149:84-90
32. Li C, de Celis Leal DR, Rana S et al (2017) Rapid Bayesian optimisation for synthesis of short polymer fiber materials. Sci Rep 7(1):1-10
33. Kikuchi S, Oda H, Kiyohara S et al (2018) Bayesian optimization for efficient determination of metal oxide grain boundary structures. Phys B Condens Matter 532:24-28
34. Xue D, Balachandran PV, Hogden J et al (2016) Accelerated search for materials with targeted properties by adaptive design. Nat Commun 7(1):1-9
35. Vahid A, Rana S, Gupta S et al (2018) New bayesian-optimization-based design of high-strength 7xxx-series alloys from recycled aluminum. JOM 70(11):2704-2709
36. Herbol HC, Hu W, Frazier P et al (2018) Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization. NPJ Comput Mater 4(1):51. https://doi.org/10.1038/s41524-018-0106-7
37. Herbol HC, Poloczek M, Clancy P et al (2020) Cost-effective materials discovery:Bayesian optimization across multiple information sources. Mater Horiz 7(8):2113-2123

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A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

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