Potential role of machine learning techniques for modeling the hardness of OPH steels

Khalaj, Omid; Ghobadi, Moslem; Zarezadeh, Alireza; Saebnoori, Ehsan; Jirková, Hana; Chocholatý, Ondřej; Svoboda, Jiřı́

doi:10.1016/j.mtcomm.2020.101806

Cited by 13 publications

(10 citation statements)

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“…The number of neurons in each hidden layer and the number of hidden layers have a significant role in the efficiency of an ANN model. Besides, other essential factors for constructing a suitable ANN structure are the transfer function and training algorithm [33]. In this study, one hidden layer is used as a suitable framework for an ANN structure.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Several topologies were built at various neurons in the hidden layer to determine the optimum number of neurons in the hidden layer. The Levenberg-Marquardt backpropagation (LMBP) training algorithm has been used extensively by researchers in the past and is widely regarded as the best training algorithm [33]. LMBP is used to find a suitable number of neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Therefore, all of the data values were set between 0.0 to 1.0. The normalized values (Xnorm) can be calculated by the following equation [33]:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…In ANFIS modeling, artificial neural networks and fuzzy system design are mixed, resulting in a robust predictive model [33]. As shown in Figure 3, the ANFIS model comprises five layers that build the fuzzy structure using the "if-then" method and employs the Takagi-Sugeno fuzzy system [38].…”

Section: Adaptive Neuro-fuzzy Inference Systems (Anifs)mentioning

confidence: 99%

“…In order to predict and describe the mechanical properties of alloys based on chemical composition and thermomechanical influence some researchers have developed statistical models. Khalaj et al [33] used machine learning techniques to predict the hardness of OPH alloys with high accuracy and investigate the effect of each parameter. The results of this research demonstrated that the hardness of OPH alloys was affected by chemical composition, mechanical alloying and heat treatment.…”

Section: Introductionmentioning

confidence: 99%

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Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

et al. 2021

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Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Therefore, all of the data values were set between 0.0 to 1.0. The normalized values (Xnorm) can be calculated by the following equation [33]:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference Systems (Anifs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%