Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning

Qin, Zijun; Wang, Zi; Wang, Yunqiang; Zhang, Lina; Li, Weifu; Liu, Jin; Wang, Zexin; Li, Zihang; Pan, Jun; Zhao, Lei; Liu, Feng; Tan, Liming; Wang, Jianxin; Han, Hua; Jiang, Liang; Liu, Yong

doi:10.1080/21663831.2020.1815093

Cited by 55 publications

(18 citation statements)

References 29 publications

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“…Other examples include, but are not limited to, Tamura et al [24] , who optimized the processing parameters of Ni-Co powder alloys based on Bayesian classification and Kim et al [45] , who analyzed the oxidation resistance of Ni-based superalloys based on an artificial NN, through which they accelerated the establishment of the relationships between alloy composition and oxidation resistance.…”

Section: Alloy Optimization and Design Of Ni-based Superalloysmentioning

confidence: 99%

“…To date, two active and highly related research areas in the application of ML in materials science and engineering are property predictions and materials discovery and design [10][11][12][13][15][16][17][18][19][20][21][22][23][24][25][26][27][28] . As such, ML algorithms involved in property prediction naturally fall in the category of supervised learning, in view of the nature of the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-guided design and development of metallic structural materials

Yu¹,

Xi²,

Pan³

et al. 2021

JMI

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In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor.

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Section: Alloy Optimization and Design Of Ni-based Superalloysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning-guided design and development of metallic structural materials

Yu¹,

Xi²,

Pan³

et al. 2021

JMI

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“…The phases of Ni-based superalloys generally include γ, γ , TCP and GCP phases, where γ is the major phase yielding precipitation strengthening and its volume fraction and particle size play dominating roles in alloy performance [7,8]. Therefore, it is essential to obtain the accurate and reliable data on γ phase distribution, which can be achieved using microstructure recognition technology [9,10].…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning for Ni-Based Superalloys Microstructure Recognition on γ′ Phase

Qin

et al. 2022

Materials

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Ni-based superalloys are widely used to manufacture the critical hot-end components of aviation jet engines and various industrial gas turbines. The analysis of Ni-based superalloys microstructures is an important research task during the design and development of superalloys. The material microstructure information can only be understood by experts in the long history. Image segmentation and recognition are developing techniques for accelerating the microstructure analysis automatically. Although deep learning techniques have achieved satisfactory performance, they usually suffer from generalization, i.e., performing worse on a new dataset. In this paper, a deep transfer learning method which just needs a small number of labeled images is proposed to perform the microstructure recognition on γ′ phase. To evaluate the effectiveness, we homely prepare two Ni-based superalloys at temperatures 900 °C and 1000 °C, and manually annotate two datasets named as W-900 and W-1000. Experimental results demonstrate that the proposed method only needs 3 and 5 labeled images to achieve state-of-the-art segmentation accuracy during the transfer from W-900 to W-1000 and the transfer from W-1000 to W-900, while enjoying the advantage of fast convergence. In addition, a simple and effective software for the Ni-based superalloys microstructure recognition on γ′ phase is developed to improve the efficiency of materials experts, which will greatly facilitate the design of new Ni-base superalloys and even other multicomponent alloys.

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“…Minor elements including Zr, B and Hf are also indispensable for the tailoring of mechanical properties by purifying or stabilizing grain boundaries [ 14 , 15 , 16 , 17 , 18 ]. Strengthening via solid solution strengthening and precipitation plays a dominant role in improving alloy strength, while excessive refractory elements lead to the formation of topologically close-packed (TCP) phases, which are detrimental to mechanical properties [ 19 , 20 , 21 ]. Therefore, to meet the requirement for the increased service temperature of disks, there is a trend of adding more γ′-forming elements to enhance the precipitation strengthening for P/M superalloys, and the γ′ fraction may approach nearly 60% [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Solution Temperature on Tensile Properties of a High γ′ Volume Fraction P/M Superalloy

et al. 2022

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To meet the pressing needs concerning the optimization of the performance of powder metallurgy (P/M) superalloys for turbine disc applications, the effects of solution temperature on a novel high γ′ volume fraction P/M superalloy FGH 4107 were investigated. The results indicated that the size of the γ′ precipitates decreased dramatically as the solution temperature increased from 1160 to 1200 °C. Theoretical calculations showed that the precipitation strengthening played a dominant role in enhancing the strength of the high γ′ volume fraction P/M superalloy, and a higher solution temperature was beneficial for the modification of the γ′ phase distribution during the following cooling and aging process.

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Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning

Cited by 55 publications

References 29 publications

Machine learning-guided design and development of metallic structural materials

Machine learning-guided design and development of metallic structural materials

Deep Transfer Learning for Ni-Based Superalloys Microstructure Recognition on γ′ Phase

Effects of Solution Temperature on Tensile Properties of a High γ′ Volume Fraction P/M Superalloy

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