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Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

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Abstract

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 A thorough data collection is created using both unpublished data and available literature and then analyzed to select twenty input parameters to feed the NN model.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.

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Abbreviations

Af:

Austenite finish temperature

Ap:

Austenite peak temperature

As:

Austenite start temperature

HTSMAs:

High-temperature shape memory alloys

LPBF:

Laser powder bed fusion

LTSMAs:

Low-transformation-temperature shape memory alloys

Mf:

Martensite finish temperature

ML:

Machine learning

MLP:

Multilayer perceptron

Mp:

Martensite peak temperature

Ms:

Martensite start temperature

NN:

Neural network

SMAs:

Shape memory alloys

SDA:

Suspended droplet alloying

TTs:

Transformation temperatures

TH:

Transformation hysteresis

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Authors and Affiliations

  1. Mechanical Industrial and Manufacturing Engineering, University of Toledo, Toledo, USA

    H. Abedi, K. S. Baghbaderani, M. Nematollahi, F. Kordizadeh, A. Qattawi & M. Elahinia

  2. Mechanical Engineering, University of California, Merced, USA

    A. Alafaghani

  3. School of Metallurgy and Materials, University of Birmingham, Birmingham, UK

    M. M. Attallah

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  1. H. Abedi
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  2. K. S. Baghbaderani
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  3. A. Alafaghani
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Correspondence to A. Qattawi.

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Appendix

Appendix

NiTiHf data set: the description of the most important features, the number of data points, and the related references.

Data set description

No. of data sets

References

The study implemented the suspended droplet alloying (SDA) fabrication method. This method showed to increase the TTs

7

Ref 87

The work arc melted the NiTiHf with additional post-processing like homogenization, extrusion, solutionizing, and several additional processes at different temperatures. The work demonstrates how each processing step changes the transformation temperatures

47

Ref 76

The material Arc melted and homogenized before TT measurement with the DSC instrument

5

Ref 96

The study published in 1995 is focused on the ternary NiTiHf arc melted and homogenized. The TTs are measured for the different atomic percentages of NiTiHF and reported. Exploring the chemical composition effect on TTs is less studied which is the core of this work. A part of the data shows the effect of material heating with different temperatures on TTs for Ni49Ti41Hf10. This study reported only the martensitic and austenitic peak temperatures. The rest of the outputs are assumed to be similar to the typically DSC results reported in this article. The data can be aggregated in the data set as the arc melting, homogenization, and DSC measurements are the standard methods that exist even before the data of this publication

24

Ref 58

The study is focused on Ni50.8Ti29.2Hf20 and Ni49.8Ti30.2Hf20 Materials

Material fabricated by induction skull melting followed by homogenization and extrusion. Then, the extruded wires were solutionized. After that, the materials were subjected to different heat treatments at different temperatures, such as heating, hot rolling, further solutionizing, and aging. Finally, the TTs were measured by the DSC technique for different conditions and reported in the article

26

Ref 73

This study reports the additive manufacturing of Ni50.4Ti29.6Hf20 with a selective laser melting technique. Only one data set is added to the study

1

Ref 77

In this study, the Ni50TiHf are studied with different amounts of Ti and Hf. The material is an electric arc melted and re-melted three times for homogenization, subsequent hot upsetting by 5-10% in a press, long-term annealing at 1073 K in argon, and quenching.

7

Ref 89

This study has a significant effect on the data sets as it presents the NiTiHf shape memory alloy with low transformation temperatures. The study discusses that if the Hf amount is in the range lower than 12%, the NiTiHf can show very low transformation temperature, and the related data are added to the article. In this study, only the martensitic finish transformation temperature is reported. The rest of the TTs are assumed based on the average difference of the TTs of the whole data set

15

Ref 62

This study reported the TTs amounts for Ni50TiHf, Hf amounts of 6, 8, and 10%. The materials are prepared with arc melting, followed by hot rolling, aging, or annealing, and the related TTs are plotted. The effect of the oxygen level is discussed for NiTiHf. The works (Ref 62) and (Ref 90) are presenting the TTs for Ni50.3Ti39.7Hf10 and Ni50Ti40Hf10 with hot rolling or without hot rolling, resulting in more than 100 °C differences in TTs

12

Ref 90

The study implemented the melt spinning fabrication approach, followed by rapid quenching. Ni48.9TiHf with 5 different atomic percentages of Hf ranging from 8 to 20% are considered.

5

Ref 88

In this study, Ni50.3Ti29.7Hf20 was manufactured with induction melting followed by homogenization at 1050 °C for 72 h and then extruded at 900 °C. The material then aged at different temperatures ranging from 300 to 900 °C, and the TTs were reported. Results confirmed a continuous TT decrease for aging temperature 400 °C and a continuous TT increase for aging with 500 °C with increasing the aging time.

8

Ref 74

In this article, three different NiTiHf atomic percentages are investigated for the TT curves. The effect of thermal cycling is also investigated. Material heat is treated up to 60 times at 800 or 900 °C. The study concluded that the TTs would become stable after 20 thermal cycles

3

Ref 64

In this article, NiTi70Hf material with different atomic percentages of the Ni and Hf are fabricated by Arc Melting, and the related TTs are reported. The study is one of the rare ones which present Hf atomic percentages lower than 10%

3

Ref 97

The study measured the TTs for Ni38Ti50Hf12. This study considers the Ternary NiTiZr and the effect of thermal cycling on the TTs

1

Ref 98

In this study, the Ni50.3Ti34.7Hf15 is fabricated by induction melting, homogenized at 1050 °C, hot extruded at 900 °C e (7:1), heat-treated at 900 °C for 1 h and then aged at various temperatures and times. This study discusses the effect of aging on the TTs

5

Ref 75

In this work, Ni50.3Ti34.7Hf15 is fabricated through arc melting, homogenization, solution treatment, hot rolling, and aging at 450 or 550 °C at different timing from 0.5 to 72 h. The data added to the model are the one related to 3 h aging. It is observed that the aging timing of more than 10 did not result in any significant changes in TTs

3

Ref 85

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Abedi, H., Baghbaderani, K.S., Alafaghani, A. et al. Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures. J. of Materi Eng and Perform 31, 10258–10270 (2022). https://doi.org/10.1007/s11665-022-06995-y

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  • DOI: https://doi.org/10.1007/s11665-022-06995-y

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