Property optimization of TRIP Ti alloys based on artificial neural network
- Oh, Jeong Mok; Narayana, P. L.; Hong, Jae-Keun; Yeom, Jong-Taek; Reddy, N. S.; Kang, Namhyun; Park, Chan Hee
- Issue Date
- ELSEVIER SCIENCE SA
- Machine learning; Artificial neural network; Alloy design; Transformation-induced plasticity; Titanium alloy
- JOURNAL OF ALLOYS AND COMPOUNDS, v.884
- Journal Title
- JOURNAL OF ALLOYS AND COMPOUNDS
- Transformation-induced plasticity (TRIP) Ti alloys are promising structural materials that offer high strength and ductility. However, these alloys often include heavy, expensive, and high-melting-point beta stabilizing elements such as V, Nb, Mo, and W. Herein, an artificial neural network (ANN) was used to develop a Ti-Al-Fe-Mn-based TRIP alloy comprising lighter and/or cheaper elements. The ANN model was trained with 30 experimental tensile datasets for heat-treated (830-920 degrees C) Ti-4Al-2Fe-xMn (x = 0-4 wt%) alloys, and used to generate 400 tensile datasets with more finely tuned composition and temperature intervals. Based on the predicted data, an 883 degrees C-heat-treated Ti-4Al-2Fe-1.4Mn alloy was produced (conditions not used in the training datasets), which exhibited ultra-high specific strength (289 MPamiddotcm3/g) and high elongation (34%). Thus, the ANN approach successfully led to the development of a new alloy while minimizing the number of labor-intensive and time-consuming experiments. (c) 2021 Elsevier B.V. All rights reserved.
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