Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network

Zhang, Zhao Hui; Yan, Dong Na; Ju, Jian Tao; Han, Ying

doi:10.4028/www.scientific.net/msf.724.351

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…For the determination of the lower and upper value of the number of hidden neurons, in order not to conduct an exhaustive search, the recommendations from some empirical relationships were taken into consideration. In [23], it was proposed that the number of hidden neurons could be approximated as follows:…”

Section: Methodology Of Results Analysismentioning

confidence: 99%

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

et al. 2019

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Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

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Section: Methodology Of Results Analysismentioning

confidence: 99%

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

et al. 2019

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“…For this type of neural network, it is derived from the aforementioned literature than a single hidden layer is sufficient. However, the size of hidden layer is determined by the approach described in [1], in which the number of neurons is approximated by the following formula:…”

Section: Methodsmentioning

confidence: 99%

“…Several notable works in this field have been presented in the past. Zhang et al [1] used an artificial neural network model to predict the flow stress of a high alloyed austenitic stainless steel. The model was used in order to study the hot deformation behavior of this particular metal.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Aluminum Alloy 7017 Using Artificial Neural Networks

Karkalos¹,

Markopoulos²

2017

The Publications of the MultiScience - XXXI. MicroCAD International Scientific Conference

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“…The most well-recognized formula is from Zhang et al [39]. Therefore, based on the trial-and-error method, 11 single-hidden-layer ANN models for predicting TCS were constructed and the number of neurons in the hidden layer ranged from 3 to 13.…”

Section: Simple Ann Modelmentioning

confidence: 99%

“…The number of optimization objects (connection weights and biases) is consistent with the length of the chromosomes in the GA. For the ANN structure (4-14-11-1) identified above, the connection weights and biases of the model are calculated by stratification and the total number is ð4 × 14Þ + ð14 × 11Þ + ð11 × 1Þ + ð14 + 11 + 1Þ = 247, where (4 × 14) is the connection weights between the 4 input neurons and Zhang [39] ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi…”

Section: Ann Initial Connection Weight and Biasmentioning

confidence: 99%

Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

et al. 2021

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Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

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Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network

Cited by 7 publications

References 11 publications

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Prediction of Mechanical Properties of Aluminum Alloy 7017 Using Artificial Neural Networks

Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

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