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<p>This paper details the experimental characterization techniques utilized to establish the shape memory characteristics of two new NiTiHf shape memory alloys (SMAs) designed by machine learning (ML). Specifically, a multi-layer feed-forward neural network (MLFFNN) framework was developed with the aim of finding the optimum NiTiHf shape memory alloy (SMA) composition that exhibits an austenite finish temperature (A<sub>f</sub>) beyond 400&#xa0;°C with a stable reversible phase transformation behavior. The predicted Ni<sub>49.7</sub>Ti<sub>26.6</sub>Hf<sub>23.7</sub> and Ni<sub>50</sub>Ti<sub>27</sub>Hf<sub>23</sub> alloys, with respective predicted A<sub>f</sub> values of 424 and 401&#xa0;°C, were cast by vacuum arc melting (VAM), and the validation experiments revealed that the Ni<sub>49.7</sub>Ti<sub>26.6</sub>Hf<sub>23.7</sub> SMA exhibited a 404&#xa0;°C A<sub>f</sub>. In order to overcome the difficulties associated with the small size of the samples manufactured by VAM, further experiments utilizing a Vickers indenter adopting a heated plate and a 3D laser scanning microscope were carried out, demonstrating that both Ni<sub>49.7</sub>Ti<sub>26.6</sub>Hf<sub>23.7</sub> and Ni<sub>50</sub>Ti<sub>27</sub>Hf<sub>23</sub> SMAs exhibited a stable and reversible martensitic phase transformation. Overall, the combined ML-based alloy design and experimental validation effort presented herein opens a venue for exploiting new alloy systems to address challenging materials problems in a timely and cost-efficient manner.</p>

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Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • D. Canadinc,
  • E. J. Breitbach,
  • A. A. Catal

摘要

This paper details the experimental characterization techniques utilized to establish the shape memory characteristics of two new NiTiHf shape memory alloys (SMAs) designed by machine learning (ML). Specifically, a multi-layer feed-forward neural network (MLFFNN) framework was developed with the aim of finding the optimum NiTiHf shape memory alloy (SMA) composition that exhibits an austenite finish temperature (Af) beyond 400 °C with a stable reversible phase transformation behavior. The predicted Ni49.7Ti26.6Hf23.7 and Ni50Ti27Hf23 alloys, with respective predicted Af values of 424 and 401 °C, were cast by vacuum arc melting (VAM), and the validation experiments revealed that the Ni49.7Ti26.6Hf23.7 SMA exhibited a 404 °C Af. In order to overcome the difficulties associated with the small size of the samples manufactured by VAM, further experiments utilizing a Vickers indenter adopting a heated plate and a 3D laser scanning microscope were carried out, demonstrating that both Ni49.7Ti26.6Hf23.7 and Ni50Ti27Hf23 SMAs exhibited a stable and reversible martensitic phase transformation. Overall, the combined ML-based alloy design and experimental validation effort presented herein opens a venue for exploiting new alloy systems to address challenging materials problems in a timely and cost-efficient manner.