Prediction of nanoindentation creep behavior of tungsten-containing high entropy alloys using artificial neural network trained with Levenberg–Marquardt algorithm - Dewangan, Sheetal Kumar; Sharma, Ashutosh; Lee, Hansung; Kumar, Vinod; Ahn, Byungmin (Journal of Alloys and Compounds, 2023)
This paper describes the synthesis of tungsten-containing high-entropy alloys (HEAs). The synthesis method involves a powder metallurgy process, and spark plasma sintering (SPS) is used to compact the powder. XRD and SEM analyses of synthesized HEAs showed that the main phases formed were body-centered and face-centered cubic phases, and a sigma phase was also observed after sintering at 900 ℃. Furthermore, nanoindentation showed that the introduction of tungsten in HEA resulted in high hardness and elastic modulus, which ranged from 8.31 to 13.57 GPa and 197.21–209.43 GPa respectively. The indentation creep behavior was ascertained at room temperature. The HEAs exhibited a significant benchmark after the addition of a specific amount of W for further investigation because of their lower creep rate. Experimental creep displacement data were used for modeling by artificial neural networks (ANNs) in which the training has been performed by the Levenberg–Marquardt algorithm. The experimental creep displacement data and the ANN model predictions have an excellent agreement. The ANN model is reliable and can accurately forecast the room temperature creep behavior of HEAs. HEAs are promising candidates for use in elevated and wear-resistance applications, owing to their unique combination of high hardness and high creep resistance.
Keywords: Artificial neural network modeling; Creep behaviors; Creep displacement; High entropy alloys; High hardness; Indentation creep; Levenberg-Marquardt algorithm; Machine-learning; Nanoindentation creeps; Synthesis method; Artificial neural network; High entropy alloy; Indentation creep; Machine learning; Powder metallurgy