The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

Mehrpouya, Mehrshad; Gisario, Annamaria; Nematollahi, Mohammadreza; Rahimzadeh, Atabak; Baghbaderani, Keyvan Safaei; Elahinia, Mohammad

doi:10.1016/j.mtcomm.2021.102022

Cited by 37 publications

(20 citation statements)

References 44 publications

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“…The primary input parameters were the laser power, hatch spacing, and scanning speed. In similar studies [19,20], an ANN model was used to construct a surrogate map between the laser parameters and outputs such as temperature, strains, etc., for an AM build part. Recently, surrogate models have gained popularity for UA in the AM process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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“…There are previous studies that shed light on the use of various ML systems in designing SMA and the resultant material and response characteristics(Ref 59,75-78) (Ref 79). A few works focused on using the ML approaches for NiTiHf material to predict the TTs (Ref 59,76,80). NN model is used in predicting the NiTi material properties such as TTs and strain recovery ratio with limited data points (Ref 80), the work focused on relating the four fabrication process parameters, of NiTi material to the strain recovery ratio and TTs.…”

Section: Machine Learning To Design the Nitihf Ttsmentioning

confidence: 99%

Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

Abedi

Qattawi

2021

Preprint

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Machine Learning (ML) techniques enabled the effective correlation of alloy characteristics. In this paper, an ML Neural Network (NN) model is proposed to predict the transformation temperatures (TTs) of NiTiHf for a given combination of elemental composition and processing parameters within an acceptable confidence interval. The main challenge in NiTiHf TTs prediction is the high dimensional dependency of TTs to various parameters as well as not fully clear governing physics. Considerable experimental research was performed from 1995 to design the NiTiHf TTs which in turn paved the path to feed the ML algorithms which are now presenting an excellent approach for material design. A thorough data set is constructed from both unpublished data and available literature and then analyzed to select twenty input parameters to feed the NN model. To model and forecast the TTs of NiTiHf with a wide range of TTs up to 800°C, a total of 173 data points are gathered, verified, and selected. The model's overall determination factor (R2 )was 0.92, indicating the viability of the proposed NN model in showing the link between material composition, processing factors, and then defining 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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“…With the addition of the third or even the fourth elements such as Au, Pd, Pt, Hf and Zr, Ms of NiTi-based alloys can be increased well above 100 °C [4][5][6][7][8][9]. In practice, SMAs with Ms above 100 °C are classified as high temperature shape memory alloys (HTSMAs) [7] and have been extensively explored over the past decade [10][11][12][13][14][15][16][17]. In particular, NiTibased HTSMAs have drawn the attention of various industries such as space and oil sectors [7,18], thanks to their elevated transformation temperatures up to 1100 °C, high strength over 2 GPa, and decent functional properties.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

“…The performance evaluation of the model was conducted through the mean absolute error (MAE), RMSE, and correlation coefficient (CC), which are reported as 0.4449, 0.8081 and 0.9999, respectively [36]. In another work [12], the process parameter optimization for additively manufactured Ni 50.4 Ti 29.6 Hf 20 by selective laser melting (SLM) was achieved through an ANN model with an R 2 (refers to Eq. 2 in Sect.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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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The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

Cited by 37 publications

References 44 publications

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

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

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