Prediction of amorphous forming ability based on artificial neural network and convolutional neural network
Source abstract
Abstract
Using a trial and error method to measure amorphous forming ability in the experiment is a complex and time-consuming process. Therefore, it is necessary to devise a method that can rapidly and accurately predict the amorphous forming ability. In this study, two models, artificial neural network and convolutional neural network, are proposed for the prediction of amorphous forming ability of various amorphous alloys. The prediction accuracy of the two models reached 0.77623 and 0.71693, respectively, both of which were more than 19% higher than the reported prediction accuracy of the 13 criteria. This result shows that artificial neural network and convolutional neural network models can accurately predict the amorphous forming ability of a variety of amorphous alloys and provide theoretical guidance for the development and preparation of amorphous alloys.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metallic Glasses and Amorphous Alloys · Phase-change materials and chalcogenides · Cultural and Historical Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
Show source-provided references
1. Influence of powder characteristics on the structural and the mechanical properties of additively manufactured Zr-based bulk metallic glass10.1016/j.matdes.2021.109976 ↗
2. Thermal processing map and thermoplastic forming map of Zr-based bulk metallic glass in the supercooled liquid region10.1016/j.jnoncrysol.2021.121008 ↗
3. Improving the glass-forming ability and plasticity of a TiCu-based bulk metallic glass composite by minor Ta doping10.1016/j.jallcom.2021.161054 ↗
4. Critical feature space for predicting the glass forming ability of metallic alloys revealed by machine learning10.1016/j.chemphys.2020.110898 ↗
5. A new mathematical expression for the relation between characteristic temperature and glass-forming ability of metallic glasses10.1016/j.jnoncrysol.2019.119829 ↗
6. A new criterion for predicting the glass-forming ability of alloys based on machine learning10.1016/j.commatsci.2020.110259 ↗
7. A new glass-forming ability criterion for bulk metallic glasses10.1016/S1359-6454(02)00166-0 ↗
8. A new correlation between the characteristics temperature and glass-forming ability for bulk metallic glasses10.1007/s10973-018-7050-0 ↗
9. Stabilization of metallic supercooled liquid and bulk amorphous alloys10.1016/S1359-6454(99)00300-6 ↗
10. New types of deep neural network learning for speech recognition and related applications: an overview10.1109/ICASSP.2013.6639344 ↗
11. Efficient English text classification using selected Machine Learning Techniques10.1016/j.aej.2021.02.009 ↗
12. Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers10.1109/ACCESS.2020.2989857 ↗
13. Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms10.1016/j.jnoncrysol.2021.121000 ↗
14. Prediction of critical cooling rate for glass forming alloys by artificial neural network10.1016/j.matdes.2013.06.012 ↗
15. A machine learning approach for engineering bulk metallic glass alloys10.1016/j.actamat.2018.08.002 ↗
16. A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses10.1016/j.jallcom.2021.160040 ↗
17. Determination of glass forming ability of bulk metallic glasses based on machine learning10.1016/j.commatsci.2021.110480 ↗
18. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets10.1016/j.commatsci.2018.05.014 ↗
19. Correlation between reduced glass transition temperature and glass forming ability of bulk metallic glasses10.1016/S1359-6462(99)00417-0 ↗
20. A new criterion for evaluating the glass-forming ability of bulk metallic glasses10.1016/j.msea.2006.06.053 ↗
21. A new criterion for evaluating the glass-forming ability of bulk glass forming alloys10.1016/j.jallcom.2007.05.037 ↗
22. A thermodynamic approach to assess glass-forming ability of bulk metallic glasses10.1016/S1003-6326(08)60438-0 ↗
23. On the parameters to assess the glass forming ability of liquids10.1016/j.jnoncrysol.2005.03.006 ↗
24. New criterion of glass forming ability for bulk metallic glasses10.1063/1.2718286 ↗
25. Relation of various GFA indicators to the critical diameter of Zr-based BMGs10.1016/j.jallcom.2014.11.112 ↗
26. New glass forming ability criterion derived from cooling consideration10.1016/j.intermet.2010.06.012 ↗
27. Evolution of glass forming ability indicator by genetic programming10.1016/j.commatsci.2016.02.037 ↗