Modelling of microstructure and mechanical properties of steel using the artificial neural network

Kusiak, J.; Kuziak, R.

doi:10.1016/s0924-0136(02)00278-9

Cited by 67 publications

(34 citation statements)

References 6 publications

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“…In addition to the standard physical models for phase distribution predictions in steels, another approach based on Artificial Neural Networks (ANN) has also been explored in the last decade in the field of steel processing [21][22][23][24]. Promising results have been obtained from the models developed using ANN not only for phase distribution prediction but also for other classes of problems in the field of materials processing and manufacturing [25][26][27][28].…”

Section: Research Approach and Theorymentioning

confidence: 99%

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Chokshi

Dashwood

Hughes

2017

Computers & Structures

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Abstract:Because of demand for lower emissions and better crashworthiness, the use of hot stamped 22MnB5 boron steel has greatly increased in manufacturing of automobile components. However, for many applications it is required that only certain regions in hot stamped parts are fully hardened whereas other regions need be more ductile. The innovative process of tailored hot stamping does this by controlling the localized microstructures through tailored cooling rates by dividing the tooling into heated and cooled zones. A barrier to optimal application of this technique is the lack of reliable phase distribution prediction model for the process.We present a novel Artificial Neural Network (ANN) based phase distribution prediction model for tailored hot stamping. The model was developed and validated using data generated from extensive thermo-mechanical physical simulation experiments and instrumented nanoindentation based phase quantification method. Advanced statistical techniques were used for preventing overfitting, for making the optimal use of available experimental data and for quantification of prediction uncertainty. The final predictions made by the ANN model during its independent validation have shown good agreement with the experimentally generated data and have a RMS prediction error of just 7.7%, which is a significant improvement over the existing models.

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Section: Research Approach and Theorymentioning

confidence: 99%

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Chokshi

Dashwood

Hughes

2017

Computers & Structures

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“…Artificial neural network (ANN) approach has been an eminent type of evolutionary computation method in the last decades, which allowed researchers to build mathematical models of neurons to simulate the neural behavior. ANN technique has been adapted for a large number of applications in different scientific fields to achieve complex decision-making tasks, without any need to prior programming [6][7][8]. In process engineering, ANN is a good alternative to conventional experiential modeling based on polynomial and linear regressions [9].…”

Section: Introductionmentioning

confidence: 99%

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

2013

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“…However, it is commonly deemed to be limited for solving the complicated non-linear problems [9]. Recently, the backpropagation neural (BPN) network, a powerful tool in the simulation and control of various non-linear processes, has been extensively applied in the field of modeling the deformation constitutive relation [10][11] as well as forecasting the content of different phases and average grain sizes [12][13][14]. Furthermore, the usefulness of the BPN network model for solving complicated nonlinear problems has been proved.…”

Section: Introductionmentioning

confidence: 99%

A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing

Wang

Liu

et al. 2010

Int J Miner Metall Mater

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The back-propagation neural (BPN) network was proposed to model the relationship between the parameters of the dieless drawing process and the microstructures of the QSi3-1 silicon bronze alloy. Combined with image processing techniques, grain sizes and grain-boundary morphologies were respectively determined by the quantitative metallographic method and the fractal theory. The outcomes obtained show that the deformed microstructures exhibit typical fractal features, and the boundaries can be characterized quantitatively by fractal dimensions. With the temperature of 600-800°C and the drawing speed of 0.67-1.00 mm·s −1 , either a lower temperature or a higher speed will cause a smaller grain size together with an elevated fractal dimension. The developed model can be capable for forecasting the microstructure evolution with a minimum error. The average relative errors between the predicted results and the experimental values of grain size and fractal dimension are 3.9% and 0.9%, respectively.

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Modelling of microstructure and mechanical properties of steel using the artificial neural network

Cited by 67 publications

References 6 publications

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

A fractal-based model for the microstructure evolution of silicon bronze wires fabricated by dieless drawing

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