Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting

Wróbel, Joanna; Kulawik, Adam; Bokota, A.

doi:10.1515/afe-2015-0022

Cited by 6 publications

(4 citation statements)

References 10 publications

(6 reference statements)

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“…1: The geometry of the presented steel element with the sensors. (Wróbel et al, 2015). The data from numerical simulations were divided in half and assigned to the learning and testing sets.…”

Section: Solution Of the Problemmentioning

confidence: 99%

“…The one-way multilayer perceptron with sigmoidal neurons was used. The network was created using one input layer (26 neurons), two hidden layers (20 and 10 neurons) and one output layer (1 neuron) (Wróbel et al, 2015). To train the network, the backpropagation algorithm was used.…”

Section: Solution Of the Problemmentioning

confidence: 99%

“…Fig. 3: The input data for the neural network -the required distribution of the temperature (Wróbel et al, 2015).…”

Section: Solution Of the Problemmentioning

confidence: 99%

See 2 more Smart Citations

Controlling the size of heat affected zone for the axisymmetric steel element with the use of ANN

2018

Engineering Mechanics

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The article is a continuation of the considerations on the heat treatment of steel bars. In the paper, a method of automatic velocity choice of a heat source with the use of an artificial neural network is presented. The heat treatment process was performed for the long axisymmetric elements with variable diameters made from medium carbon steel. The two cases of change, linear and step, of the radius of the steel element are considered. It is assumed that the velocity of the heat source is modified in the process and is updated in real time according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source. The main task of the model is to control the assumed values of temperature at constant parameters of the heat source such as radius and power. The aim of the system, assuming that the heat source has constant values of power and radius, is to provide the established values of temperature on the surface of a quenching element. Therefore, the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The artificial neural network is trained on the basis of results obtained from copyrighted numerical model of thermal phenomena (stationary task). To solve the steady heat transfer equation (axisymmetric task) with a convective term the finite element method is used. To verify the trained artificial neural network numerical simulations are performed based on the solution of the unsteady heat transfer equation.

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Section: Solution Of the Problemmentioning

confidence: 99%

Section: Solution Of the Problemmentioning

confidence: 99%

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Controlling the size of heat affected zone for the axisymmetric steel element with the use of ANN

2018

Engineering Mechanics

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“…This ANN-based tool box can successfully predict the required process parameters of the quenching process for a desired material property considering minimum energy consumption, thereby saving energy and resources. Some previous studies have successfully used neural networks for specific heat treatment investigations [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Modeling of the Heterogeneous Gas Quenching Process for Steel Batches Based on Numerical Simulations and Experiments

Narayan,

Landgraf,

Lampke

et al. 2024

Dynamics

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High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production.

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Artificial Slags with Modulated Properties for Controlled Nickel Dissolution in Smelting Process

Urtnasan,

Kumar,

Wang

2024

Trans Indian Inst Met

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In pyrometallurgy process involving drying, calcination and reductive smelting steps, Saprolite ore is subjected in an electric arc furnace to produce Fe–Ni alloy and Ni matte. To optimize the nickel smelting step, the slag consisted of MgO–SiO2–FeO and having a lower viscosity and high fluidity is required. In this study, a number of new slag samples with varied degree of compositions are manufactured and used to examine the effect of their viscosity on nickel dissolution. Further correlation between the slag basicity and FeO addition on the smelting was also tested and simulated using Fe–Ni alloy from saprolite ore. As per the observations, the nickel dissolution increases with the increased content of FeO at a certain degree of activity which in turn can cause oxidation of nickel during the process. The slags with the ratio of MgO/SiO2 = 0.4–0.6, 30% fraction of FeO, and a viscosity of 1.8–1.06 poise in the olivine zone are found effective for the lowest dissolution rate of nickel with 0.021, 0.022, and 0.018 wt.%. The lowest dissolution rate is recorded for the slag with MgO/SiO2 ratio about 0.6. These slags with controlled amount of constituents could be used to recover precious metal from nickel smelter at a lower dissolution rate and thereby allowing to reduce the waste of the metal during smelting process.

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Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting

Cited by 6 publications

References 10 publications

Controlling the size of heat affected zone for the axisymmetric steel element with the use of ANN

Controlling the size of heat affected zone for the axisymmetric steel element with the use of ANN

Artificial Intelligence Modeling of the Heterogeneous Gas Quenching Process for Steel Batches Based on Numerical Simulations and Experiments

Artificial Slags with Modulated Properties for Controlled Nickel Dissolution in Smelting Process

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