Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys

2019 ◽  
Vol 33 (08) ◽  
pp. 1950055 ◽  
Author(s):  
Daichi Minami ◽  
Tokuteru Uesugi ◽  
Yorinobu Takigawa ◽  
Kenji Higashi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
First Principles ◽  
Accurate Method ◽  
First Principles Calculations ◽  
Simple Linear Regression ◽  
Artificial Neural

A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression.

Rapid Characterization of Shape Memory Alloy Material Parameters Using Computational Intelligence Methods

2013 ◽  
Author(s):  
James V. Henrickson ◽  
Kenton Kirkpatrick ◽  
John Valasek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Memory Alloy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Training Data ◽  
Material Parameters ◽  
Influence Coefficients ◽  
Alloy Material ◽  
Artificial Neural

Shape memory alloys are capable of delivering advantageous solutions to a wide range of engineering-based problems. Implementation of these solutions, however, is often complicated by the hysteretic, non-linear, thermo-mechanical behavior of the material. Although existing shape memory alloy constitutive models are largely accurate in describing this unique behavior, they require prior characterization of the material parameters. Consequently, before thorough modeling and simulation can occur for a shape memory alloy-based project, one must first go through the process of identifying several material parameters unique to shape memory alloys. Current characterization procedures necessitate extensive experimentation, data collection, and data processing. As a result, these methods simultaneously create a high barrier of entry for engineers new to active materials and impede the advanced study of shape memory alloy material parameter evolution. This paper develops a novel method in which computational intelligence methods are used to rapidly identify shape memory alloy material parameters. Specifically, an artificial neural network is trained to identify transformation temperatures and stress influence coefficients of given shape memory alloy specimens using strain-temperature coordinates as inputs. After generating training data through the use of a constitutive model, the resulting trained artificial neural network was used to identify parameters for a number of randomly generated theoretical shape memory alloys. Results presented in the paper show that the artificial neural network was able to rapidly identify both transformation temperatures and stress influence coefficients with satisfactory accuracy. The generation of training data was then repeated using Taguchi methods. Further results presented in the paper show that the artificial neural network trained with the Taguchi-based training data yielded improved characterization accuracy while using less training data.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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