Yield strength prediction of high-entropy alloys using machine learning

Bhandari, Uttam; Rafi, Md. Rumman; Zhang, Congyan; Yang, Shizhong

doi:10.1016/j.mtcomm.2020.101871

Cited by 59 publications

(27 citation statements)

References 33 publications

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“…This phenomenon is ascribed to indentation size effect [47], and similar observations were also reported by other researchers [48,49]. The ML studies on mechanical and physical properties of HEAs were verified by comparing with the experiments [21][22][23][24][25][26][50][51][52]. The predicted Vickers hardness of C 0.1 Cr 3 Mo 11.9 Nb 20 Re 15 Ta 30 W 20 with nominal composition and experimental composition were 695 H V and 686 H V , respectively.…”

Section: Resultssupporting

confidence: 82%

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

et al. 2021

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Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.

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Section: Resultssupporting

confidence: 82%

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

et al. 2021

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“…At 800 °C, the surrogate model showed the prediction error less than 20% for only two model alloys. While in work [ 50 ] for high-entropy alloys MoNbTaTiW and HfMoNbTaTiZr at 800 °C, the prediction accuracy is 95%. Thus, our proposed model for predicting the yield stress has good accuracy for room temperature and 600 °C, but for higher temperatures its accuracy is insufficient.…”

Section: Resultsmentioning

confidence: 99%

“…Articles devoted to the prediction of strength characteristics at elevated temperatures are very rare. Bhandari et al [ 50 ] predicted yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C with high accuracy by using RF regressor model. Therefore, in this work, we have employed the machine learning method to predict mechanical properties of Al-Cr-Nb-Ti-V-Zr system RHEAs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

Клименко

Stepanov

et al. 2021

Materials

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The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling. The yield strength prediction in a wide range of temperatures (20–800 °C) was made using a surrogate model based on a support-vector machine algorithm. The yield strength at 20 °C and 600 °C was predicted quite precisely (the average prediction error was 11% and 13.5%, respectively) with a decrease in the precision to slightly higher than 20% at 800 °C. An Al13Cr12Nb20Ti20V35 alloy with an excellent combination of ductility and yield strength at 20 °C (16.6% and 1295 MPa, respectively) and at 800 °C (more 50% and 898 MPa, respectively) was produced based on the prediction.

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“…[9][10][11] The HEAs entice interest because of their promising properties which include remarkable strength, hardness, corrosion resistance, were resistance, and unusual mechanical performance at high temperatures as well as cryogenic temperature. [12][13][14][15][16][17][18][19][20][21][22] The most significant observation is that highentropy alloys often have good physical, mechanical and chemical properties. As a result of their versatility, workability, and environmental safety, HEAs have a wide range of potential applications as structural and functional materials such as thermoelectric materials magnetic materials superconducting.…”

Section: Introductionmentioning

confidence: 99%

A review on phase prediction in high entropy alloys

Soni

Sanyal

Rao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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The formation of single phase solid solution in High Entropy Alloys (HEAs) is essential for the properties of the alloys therefore, numerous approach were proposed by many researchers to predict the stability of single phase solid solution in High Entropy Alloy. The present review examines some of the recent developments while using computational intelligence techniques such as parametric approach, CALPHAD, Machine Learning etc. for prediction of various phase formation in multicomponent high entropy alloys. A detail study of this data-driven approaches pertaining to the understanding of structural and phase formation behaviour of a new class of compositionally complex alloys is done in the present investigation. The advantages and drawbacks of the various computational are also discussed. Finally, this review aims at understanding several computational modeling tools complying the thermodynamic criteria for phase formation of novel HEAs which could possibly deliver superior mechanical properties keeping an aim at advanced engineering applications.

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Yield strength prediction of high-entropy alloys using machine learning

Cited by 59 publications

References 33 publications

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

A review on phase prediction in high entropy alloys

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