A comparative study on constitutive equations and artificial neural network model to predict high-temperature deformation behavior in Nitinol 60 shape memory alloy

2015 ◽  
Vol 30 (12) ◽  
pp. 1988-1998 ◽  
Author(s):  
Xiaoyong Shu ◽  
Shiqiang Lu ◽  
Kelu Wang ◽  
Guifa Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Shape Memory Alloy ◽  
Neural Network Model ◽  
Deformation Behavior ◽  
Artificial Neural Network Model ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Image Position

Abstract

High-temperature deformation processing maps for a NiTiCu shape memory alloy

2011 ◽  
Vol 26 (19) ◽  
pp. 2484-2492 ◽  
Author(s):  
Vyasa V. Shastry ◽  
Bikas Maji ◽  
Madangopal Krishnan ◽  
Upadrasta Ramamurty
Keyword(s):  
High Temperature ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Processing Maps ◽  
Deformation Processing ◽  
Image Position

Abstract

scholarly journals Prediction of the Strength Properties of Carbon Fiber-Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Harun Tanyildizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
High Temperature ◽  
Carbon Fiber ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Lightweight Concrete ◽  
Support Vector ◽  
Artificial Neural

The artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). The carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m3). The specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. The amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. The compressive and flexural strengths of the lightweight concrete were determined as the output variables. The model results were compared with the experimental results. The best results were achieved from the artificial neural network model. The accuracy of the artificial neural network model was found at 99.02% and 96.80%.

High temperature deformation behavior and processing map for a nickel-titanium shape memory alloy

2017 ◽  
Vol 23 (5) ◽  
pp. 1002-1011 ◽  
Author(s):  
Xiang-Qian Yin ◽  
Sang-Won Lee ◽  
Yan-Feng Li ◽  
Chan-Hee Park ◽  
Xu-Jun Mi ◽  
...  
Keyword(s):  
High Temperature ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Deformation Behavior ◽  
Processing Map ◽  
Nickel Titanium ◽  
High Temperature Deformation ◽  
Temperature Deformation

scholarly journals Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

2019 ◽  
Vol 10 (3) ◽  
pp. 1081-1095 ◽  
Author(s):  
Okorie E. Agwu ◽  
Julius U. Akpabio ◽  
Adewale Dosunmu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Pressure ◽  
High Temperature ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Mean Square ◽  
High Temperature High Pressure

AbstractIn this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature.

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