Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence

Nazarahari, Alireza; Canadinç, D.

doi:10.1016/j.matchemphys.2020.123974

Cited by 19 publications

(7 citation statements)

References 47 publications

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“…We found that RBM based feature representation was better suited as an encoder for this study over a conventional autoencoder, as RBM was faster to process the large volume of video frames with standard available libraries. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) 31 – 43 . We utilized RBM as a feature extraction method to reduce the very large dimension of the video frames.…”

Section: Preparation Of the Sma And Thermal Treatmentmentioning

confidence: 99%

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Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

Dutta

Chen

Renshaw

et al. 2021

Sci Rep

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Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

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Section: Preparation Of the Sma And Thermal Treatmentmentioning

confidence: 99%

“…The change in the relative position and shape of the SMA body compared to its original position and shape under excitement, was captured in the video frames. This dynamically changing information about shape and position were correlated with the separately measured generated force for using the proposed predictive modelling 23 – 31 .…”

Section: Introductionmentioning

confidence: 99%

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

Dutta

Chen

Renshaw

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…We found that RBM based feature representation was better suited as an encoder for this study over a conventional autoencoder, as RBM was faster to process the large volume of video frames with standard available libraries. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [31][32][33][34][35][36][37][38][39][40][41][42][43]. We utilized RBM as a feature extraction method to reduce the very large dimension of the video frames.…”

Section: Chemical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision Based Supervised Restricted Boltzmann Machine Helps to Actuate Novel Shape Memory Alloy Accurately

Dutta

Chen

Renshaw

et al. 2021

Preprint

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Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

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“…For the development of copper alloys with targeted properties such as ultimate tensile strength and electrical conductivity, propertyoriented machine learning-based design strategies have been applied [11]. In a similar approach, Nazarahari et al used a multilayer feed-forward neural network to design the optimum composition of Ni-Ti superalloy for dental application [12]. The optimum composition of 51.5 at.% Ni with balance Ti showed the lowest amount of Nickel ion release into the dental cavity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Mohanty

Chandran

Sparks

2022

Preprint

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Nickel and Cobalt based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100oC. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure-property data of limited alloys and extrapolates this information into the unexplored compositional space. In the present work, we harness machine learning and provide a design strategy for finding an Nb-based alloy composition with optimized yield strength and ultimate tensile strength at high temperatures. We use a Bayesian optimization algorithm combined with domain knowledge-based material descriptors to find an optimal Nb-based quaternary and quinary alloy composition for the targeted value of mechanical strengths. Furthermore, we extend our study to multi-objective optimization to suggest an optimal alloy candidate by integrating yield strength and ultimate tensile strength into a single composite property. We developed a detailed design flow and python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

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Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence

Cited by 19 publications

References 47 publications

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

Vision Based Supervised Restricted Boltzmann Machine Helps to Actuate Novel Shape Memory Alloy Accurately

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

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