Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks

Narayana, P.L.; Lee, Sang Won; Park, Chan Hee; Yeom, Jong‐Taek; Hong, Jae‐Keun; Maurya, A.K.; Reddy, N.S.

doi:10.1016/j.commatsci.2020.109617

Cited by 30 publications

(13 citation statements)

References 16 publications

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“…Narayana designed an artificial neural network (ANN) model to correlate the complex relations among composition, temperature, and mechanical properties of steels. The ANN predictions are more accurate with experimental results as compared with the calculated properties of the existing model [14,15]. Some studies improve the performance of feature selection by choosing effective measurement indicators [16,17].…”

Section: Introductionmentioning

confidence: 89%

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Yan

2021

Metals

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Traditional mechanical properties prediction models are mostly based on experience and mechanism, which neglect the linear and nonlinear relationships between process parameters. Aiming at the high-dimensional data collected in the complex industrial process of steel production, a new prediction model is proposed. The multidimensional support vector regression (MSVR)-based model is combined with the feature selection method, which involves maximum information coefficient (MIC) correlation characterization and complex network clustering. Firstly, MIC is used to measure the correlation between process parameters and mechanical properties, based on which a complex network is constructed and hierarchical clustering is performed. Secondly, we evaluate all parameters and select a representative one for each partition as the input of the subsequent model based on the centrality and influence indicators. Finally, an actual steel production case is used to train the MSVR prediction model. The prediction results show that our proposed framework can capture effective features from the full parameters in terms of higher prediction accuracy and is less time-consuming compared with the Pearson-based subset, full-parameter subset, and empirical subset input. The feature selection method based on MIC can dig out some nonlinear relationships which cannot be found by Pearson coefficient.

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Section: Introductionmentioning

confidence: 89%

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Yan

2021

Metals

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“…In practice, most of the modelling results in steels and metal alloys presented in the literature concern neural networks with one hidden layer. There are fewer cases of using two [38][39][40][41][42][43] or more hidden layers [30,44].…”

Section: Data Set and Neural Network Topologymentioning

confidence: 99%

“…Pawar and Date [52] used seven neurons in the output layer to calculate the mechanical properties and microstructure at selected points of the steel rotor shaft. Narayana et al [40] presented the network with four neurons in the output layer of a neural network to calculate the mechanical properties of corrosion-resistant steel. Chakraborty et al [53] used six neurons in the hidden layer to calculate the phase transformation temperatures of supercooled austenite in steel.…”

Section: Dependent Variables In the Neural Modelmentioning

confidence: 99%

“…Wang et al [46] and Reddy et al [64] presented the error values independently for the training and test set. Narayana et al [40] presented the results for a different number of neurons in one and two hidden layers.…”

Section: Model Selection and Overfitting Problemmentioning

confidence: 99%

“…Examples of such simulations, including the analysis of the obtained results, can be found in many publications in the field of steels and metal alloys [29,32,40,42,46,55,60,61,64,65,68,69,72,73].…”

Section: Simulation Using Artificial Neural Networkmentioning

confidence: 99%

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Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek

Trzaska

2021

Metals

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Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

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Prediction of Mechanical Properties of Cr-Mn-N Austenitic Stainless Steel Using Machine Learning Approach

Khan,

Rao,

Ghosh

et al. 2023

Proceedings of the International Conference on Metallurgical Engineering and Centenary Celebration

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Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks

Cited by 30 publications

References 16 publications

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Prediction of Mechanical Properties of Cr-Mn-N Austenitic Stainless Steel Using Machine Learning Approach

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