Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

Abedi, Hossein; Baghbaderani, Keyvan Safaei; Alafaghani, Ala’aldin; Nematollahi, Mohammadreza; Kordizadeh, Fatemeh; Qattawi, Ala; Elahinia, Mohammad

doi:10.1007/s11665-022-06995-y

Cited by 10 publications

(4 citation statements)

References 91 publications

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“…alloy development studies [12,24,34,37,38]. Their adopted ML models have the capacity to reveal various possible input-output relationships including linear, nonlinear, polynomial, and nonparametric, which cover simple to complex relationships.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…To date, a limited effort is devoted to the ML development of NiTiHf alloys for high-temperature actuators except a few latest studies [24,37,38]. This has led to the current initiative to identify new NiTiHf alloy compositions with balanced performance in terms of Ms, transformation hysteresis TH and WO.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…Compared to the recently developed ML models on NiTiHf alloy development [24,37,38], the combination of all three data sets in this work further complicates the search for the most suitable alloys. In this work, the extracted raw data via ML is used to provide data distribution patterns among the properties.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys

Kankanamge

Reiner

et al. 2022

J Mater Sci

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With the increasing use of CubeSats in space exploration, the demand for reliable high-temperature shape memory alloys (HTSMA) continues to grow. A wide range of HTSMAs has been investigated over the past decade but finding suitable alloys by means of trial-and-error experiments is cumbersome and time-consuming. The present work uses a data-driven approach to identify NiTiHf alloys suitable for actuator applications in space. Seven machine learning (ML) models were evaluated, and the best fit model was selected to identify new alloy compositions with targeted transformation temperature (Ms), thermal hysteresis, and work output. Of the studied models, the K-nearest neighbouring ML model offers more reliable and accurate prediction in developing NiTiHf alloys with balanced functional properties and aids our existing understanding on compositional dependence of transformation temperature, thermal hysteresis and work output. For instance, the transformation temperature of NiTiHf alloys is more sensitive to Ni variation with increasing Hf content. A maximum Ms reduction rate of 6.12 °C per 0.01 at.% Ni is attained at 30 at.% Hf, and with a Ni content between 50 and 51 at.%. Graphical abstract

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys

Kankanamge

Reiner

et al. 2022

J Mater Sci

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“…The shape memory effect is their ability to undergo a reversible, non-diffusive martensitic solid-to-solid phase transformation, allowing them to return to their original shape after being triggered by some type of stimulus [2]. SMAs' key features are induced by the martensitic transformation, which is defined by the two phases' critical temperatures including austenite start temperature (A s ), austenite finish temperature (A f ), martensite start temperature (M s ), and martensite finish temperature (M f ) [3,4]. The four temperatures are crucial in the design of those smart metallic materials [5].…”

Section: Introductionmentioning

confidence: 99%

Fe-Mn-Al-Ni Shape Memory Alloy Additively Manufactured via Laser Powder Bed Fusion

Alhamdi,

Algamal,

Almotari

et al. 2023

Crystals

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Fe-Mn-Al-Ni is an Fe-based shape memory alloy (SMA) featuring higher stability and low temperature dependency of superelasticity stress over a wide range of temperatures. Additive manufacturing (AM) is a promising technique for fabricating Fe-SMA with enhanced properties, which can eliminate the limitations associated with conventional fabrication and allow for the manufacture of complicated shapes with only a single-step fabrication. The current work investigates the densification behavior and fabrication window of an Fe-Mn-Al-Ni SMA using laser powder bed fusion (LPBF). Experimental optimization was performed to identify the optimum processing window parameters in terms of laser power and scanning speed to fabricate Fe-Mn-Al-Ni SMA samples. Laser remelting was also employed to improve the characteristics of Fe-Mn-Al-Ni-fabricated samples. Characterization and testing techniques were carried out to assess the densification behavior of Fe-Mn-Al-Ni to study surface roughness, density, porosity, and hardness. The findings indicated that using a laser power range of 175–200 W combined with a scanning speed of 800 mm/s within the defined processing window parameters can minimize the defects with the material and lead to decreased surface roughness, lower porosity, and higher densification.

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Predicting Transformation Temperatures of Additively Manufactured NiTiHf Shape Memory Alloy Using Neural Network Modeling

Abedi,

Algamal,

Abdollahzadeh

et al. 2023

JOM

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Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

Cited by 10 publications

References 91 publications

Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys

Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys

Fe-Mn-Al-Ni Shape Memory Alloy Additively Manufactured via Laser Powder Bed Fusion

Predicting Transformation Temperatures of Additively Manufactured NiTiHf Shape Memory Alloy Using Neural Network Modeling

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