Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Opěla, Petr; Schindler, Ivo; Kawulok, Petr; Kawulok, Rostislav; Rusz, Stanislav; Sauer, Michal

doi:10.1016/j.matdes.2022.110880

Cited by 20 publications

(11 citation statements)

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“…In the present work, it was shown that the ordinary ANN can successfully be used to model the flow stress and inferring useful information regarding the hot deformation behaviour. Accordingly, more sophisticated machine learning techniques can be used in this regard, including evolutionary deep neural net (EvoDN2) [37,38], adaptive neuro-fuzzy inference system (ANFIS) [39], and deep and reinforcement learning of artificial neural network model [10,16], as well as multi-objective optimisation techniques such as genetic algorithm [23].…”

Section: Resultsmentioning

confidence: 99%

“…The characterisation of hot deformation behaviour during hot working is vital for developing metalforming processes [8][9][10][11], for which the appropriate constitutive equations are utilised to predict the hot deformation behaviour of the materials under the prevailing loading conditions [12,13]. For this purpose, the empirical, semi-empirical, phenomenological, and physically-based models [14,15], as well as machinelearning approaches such as artificial neural networks (ANN) models [16][17][18][19][20][21][22][23] have been proposed so far.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys

Zamani

Mirzadeh

Malekan

2023

Materials Science and Technology

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The potentials of artificial neural network (ANN) modelling as a potent machine learning approach for investigating the hot deformation behaviour of high-entropy alloys (HEAs) and multi-principal element alloys during thermomechanical processing are assessed and reviewed. Flow stress of CoCrFeNiMn (FCC Cantor alloy), HfNbTaTiZr (BCC refractory alloy), AlCoCuFeNi, and Al xCoCrFeNi alloys is accurately predicted based on the deformation temperature, strain rate, and strain. Moreover, in comparison with the limited experimental dataset, a significantly larger output dataset can be generated by ANN to gain valuable insights such as prediction of flow stress (and whole dynamic recovery/recrystallisation flow curves), elucidating the microstructural mechanisms such as dynamic precipitation reactions, and obtaining hot working parameters (e.g. deformation activation energy) for different ranges of deformation conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys

Zamani

Mirzadeh

Malekan

2023

Materials Science and Technology

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“…W. Choi et al have used the ANN approach to predict the influence of vanadium content on the microstructure and mechanical properties of low-alloyed high-strength steel [ 32 ]. P. Opela et al have applied the deep learning of an ANN-based model to describe the hot flow stress of 38MnVS6 steel [ 33 ]. Jeong et al have constructed a model for the prediction of the hot ductility region in high-Mn steel [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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The development of new lightweight materials is required for the automotive industry to reduce the impact of carbon dioxide emissions on the environment. The lightweight, high-manganese steels are the prospective alloys for this purpose. Hot deformation is one of the stages of the production of steel. Hot deformation behavior is mainly determined by chemical composition and thermomechanical parameters. In the paper, an artificial neural network (ANN) model with high accuracy was constructed to describe the high Mn steel deformation behavior in dependence on the concentration of the alloying elements (C, Mn, Si, and Al), the deformation temperature, the strain rate, and the strain. The approval compression tests of the Fe–28Mn–8Al–1C were made at temperatures of 900–1150 °C and strain rates of 0.1–10 s−1 with an application of the Gleeble 3800 thermomechanical simulator. The ANN-based model showed high accuracy, and the low average relative error of calculation for both training (5.4%) and verification (7.5%) datasets supports the high accuracy of the built model. The hot deformation effective activation energy values for predicted (401 ± 5 kJ/mol) and experimental data (385 ± 22 kJ/mol) are in satisfactory accordance, which allows applying the model for the hot deformation analysis of the high-Mn steels with different concentrations of the main alloying elements.

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“…Artificial neural networks are widely used in various predictive applications. The ability of ANN models to predict non-linear systems and the ease of their implementation contributed to their increased use for solving research problems connected with aspects, such as the prediction of, e.g., hot flow stress [30] or high-temperature deformation of steel [31], chemical composition modeling [32], industrial electrical tomography [33], electrical impedance tomography [34]. Modeling has also been applied in studies on abrasive water-jet machining.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vibration, Deflection Angle and Surface Roughness in Water-Jet Cutting of AZ91D Magnesium Alloy and Simulation of Selected Surface Roughness Parameters Using ANN

et al. 2023

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The use of magnesium alloys in various industries and commerce is increasing due to their properties such as high strength and casting properties, high vibration damping capability, good shielding of electromagnetic radiation and high machinability. Conventional machining methods can, however, pose a risk of ignition. AWJM is a safe alternative to conventional machining, but the deflection and vibration of the water jet can affect surface quality. Therefore, the aim of this study was to investigate the effects of selected AWJM parameters on the surface quality and vibration of machined magnesium alloys. Jet deflection angle, surface roughness parameters and vibration during AWJM were investigated. The findings showed that higher skewness occurred at a lower abrasive flow rate, while higher average values of the Sku roughness parameter were obtained at ma = 8 g/s in the range of 60–140 mm/min. It was also observed that higher vibration values occurred at ma = 8 g/s. The input parameters for creating an artificial neural network (ANN) model used in this study were the cutting speed vf and the mass flow rate ma. The results of this study provided valuable insights into ways of ensuring a safe and efficient machining environment for magnesium alloys. The use of ANN modeling for predicting the vibration and surface roughness of AZ91D magnesium alloy after water-jet cutting could be an effective tool for optimizing AWJM parameters.

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Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Cited by 20 publications

References 50 publications

Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys

Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Analysis of Vibration, Deflection Angle and Surface Roughness in Water-Jet Cutting of AZ91D Magnesium Alloy and Simulation of Selected Surface Roughness Parameters Using ANN

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