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

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7213
Author(s):  
Denis Klimenko ◽  
Nikita Stepanov ◽  
Jia Li ◽  
Qihong Fang ◽  
Sergey Zherebtsov
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
Strength Prediction ◽  
Support Vector ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Average Prediction Error ◽  
Calphad Modeling ◽  
Wide Range ◽  
Alloys Of The Al

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.

Yield strength prediction of high-entropy alloys using machine learning

2020 ◽  
pp. 101871
Author(s):  
Uttam Bhandari ◽  
Md. Rumman Rafi ◽  
Congyan Zhang ◽  
Shizhong Yang
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
Strength Prediction ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals High-Entropy Alloys Properties Prediction Model by Using Artificial Neural Network Algorithm

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1559
Author(s):  
Sanggyu Choi ◽  
Sung Yi ◽  
Junghan Kim ◽  
Byungsue Shin ◽  
Soongkeun Hyun
Keyword(s):  
Neural Network ◽  
Yield Strength ◽  
Mole Fraction ◽  
Prediction Error ◽  
Strength Prediction ◽  
High Entropy Alloys ◽  
Process Data ◽  
High Entropy ◽  
Post Process ◽  
The Mean

A new approach method has been studied for the efficient and accurate prediction of high-entropy alloys (HEAs) properties. The artificial neural network (ANN) algorithm was employed to predict the mechanical properties such as yield strength, microstructure, and elongation of the alloy by training from the mole fraction and post-process information that has an influence on the mechanical properties. The mean error rate of prediction for the yield strength was 19.6%. Microstructure predictions were consistent for all test data. On the other hand, the ANN model trained only with mole fraction data had a yield strength prediction error of 33.9%. Omission of post-process data caused a decrease in the accuracy. In addition, the prediction was performed with the lasso regression model in the same way. The mean error rate of the lasso model trained with only a mole fraction was 26.1%. The lasso model trained with a mole fraction and post-process data had a yield strength prediction error of 31.1%. The linear regression equation showed limitations, as the accuracy decreased as the number of independent variables increased. As there are more variables affecting metal properties, the ANN approach is more advantageous, and the more data there are, the more accuracy increases, making it possible to design HEAs alloys that are simpler and more efficient than conventional methods. This approach predicted HEAs properties using only mole fraction and post-processing information, without the need to use conventional physicochemical theories or perform derived complex calculations.

Machine learning-enabled identification of new medium to high entropy alloys with solid solution phases

2021 ◽  
Vol 197 ◽  
pp. 110623
Author(s):  
Ujjawal Kumar Jaiswal ◽  
Yegi Vamsi Krishna ◽  
M.R. Rahul ◽  
Gandham Phanikumar
Keyword(s):  
Machine Learning ◽  
Solid Solution ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals The phase selection via machine learning in high entropy alloys

2019 ◽  
Vol 37 ◽  
pp. 299-305 ◽  
Author(s):  
Nan Qu ◽  
Yichuan Chen ◽  
Zhonghong Lai ◽  
Yong Liu ◽  
Jingchuan Zhu
Keyword(s):  
Machine Learning ◽  
High Entropy Alloys ◽  
Phase Selection ◽  
High Entropy

Machine learning assisted design of FeCoNiCrMn high-entropy alloys with ultra-low hydrogen diffusion coefficients

2021 ◽  
pp. 117535
Author(s):  
Xiao-Ye Zhou ◽  
Ji-Hua Zhu ◽  
Yuan Wu ◽  
Xu-Sheng Yang ◽  
Turab Lookman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Coefficients ◽  
Hydrogen Diffusion ◽  
High Entropy Alloys ◽  
High Entropy

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals A review of Machine Learning (ML) algorithms used for modeling travel mode choice

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 32-41 ◽  
Author(s):  
Juan D. Pineda-Jaramillo
Keyword(s):  
Machine Learning ◽  
Choice Model ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Support Vector ◽  
Travel Mode ◽  
Wide Range ◽  
Travel Mode Choice ◽  
And Cluster Analysis ◽  
Transportation Research

In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

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