• Qattawi, A.
• Kordizadeh, F.
• Nematollahi, M.
• Alafaghani, A.
• Abedi, H.
• Baghbaderani, K. S.
• Elahinia, M.
• Attallah, Moataz Moataz

Abstract

<p>Data-driven techniques are used to predict the transformation temperatures (TTs) of NiTiHf shape memory alloy. A machine learning (ML) approach is used to overcome the high-dimensional dependency of NiTiHf TTs on numerous factors, as well as the lack of fully known governing physics. The elemental composition, thermal treatments, and post-processing steps that are commonly used to process NiTiHf and have an impact on the material phase transitions are used as input parameters of the neural network model (NN) to design the TTs. Such a feature selection led to the use of most of the accessible information in the literature on NiTiHf TTs, as all processing features required to be fed into the NN model. Considering most of the regular NiTiHf processing factors also enables the option of tuning additional characteristics of NiTiHf in addition to the TTs. The work is unique as all the four main TTs and their associated peak transformation temperatures are predicted to have complete control over the material phase change thresholds. Since 1995, extensive experimental research has been conducted to design NiTiHf TTs with a large temperature range of around 800 °C, paving the path for the current work’s ML algorithms to be fed. A thorough data collection is created using both unpublished data and available literature and then analyzed to select twenty input parameters to feed the NN model. To forecast the NiTiHf TTs, a total of 173 data points were gathered, verified, and selected. The model's overall determination factor (R²) was 0.96, suggesting the viability of the proposed NN model in demonstrating the link between material composition and processing factors, as well as identifying the TTs of NiTiHf alloy. The effort additionally validates the generated results against existing data in the literature. The validation confirms the significance of the proposed model.</p>

Topics

• impedance spectroscopy
• phase
• phase transition
• machine learning