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Salehebrahimnejad, B., Doniavi, A., Moradi, M., Shahbaz, M.. (1400). Optimization of Superimposed Residual Stress Components to Improve Fatigue Life of Work Roll in Hot Rolling Process Using Artificial Neural Network and Genetic Algorithm. پژوهش های زبان شناسی تطبیقی, 6(2), 97-105. doi: 10.22084/jrstan.2022.26129.1209
B. Salehebrahimnejad; A. Doniavi; M. Moradi; M. Shahbaz. "Optimization of Superimposed Residual Stress Components to Improve Fatigue Life of Work Roll in Hot Rolling Process Using Artificial Neural Network and Genetic Algorithm". پژوهش های زبان شناسی تطبیقی, 6, 2, 1400, 97-105. doi: 10.22084/jrstan.2022.26129.1209
Salehebrahimnejad, B., Doniavi, A., Moradi, M., Shahbaz, M.. (1400). 'Optimization of Superimposed Residual Stress Components to Improve Fatigue Life of Work Roll in Hot Rolling Process Using Artificial Neural Network and Genetic Algorithm', پژوهش های زبان شناسی تطبیقی, 6(2), pp. 97-105. doi: 10.22084/jrstan.2022.26129.1209
Salehebrahimnejad, B., Doniavi, A., Moradi, M., Shahbaz, M.. Optimization of Superimposed Residual Stress Components to Improve Fatigue Life of Work Roll in Hot Rolling Process Using Artificial Neural Network and Genetic Algorithm. پژوهش های زبان شناسی تطبیقی, 1400; 6(2): 97-105. doi: 10.22084/jrstan.2022.26129.1209

Optimization of Superimposed Residual Stress Components to Improve Fatigue Life of Work Roll in Hot Rolling Process Using Artificial Neural Network and Genetic Algorithm

Journal of Stress Analysis
دوره 6، شماره 2، 2022، صفحه 97-105    اصل مقاله (2.18 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22084/jrstan.2022.26129.1209
نویسندگان
B. Salehebrahimnejad1؛ A. Doniavi1؛ M. Moradi2؛ M. Shahbaz* 3
1Department of Mechanical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.
2Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.
3Department of Materials Science and Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.
چکیده
 In this paper, a semi-analytical model was proposed to superimpose the initial residual stress components on the work roll surface and subsurface to minimize the maximum value of Von-Mises Stresses (MVMS) during the hot rolling process to reduce the possibility of roll wear and increase the fatigue life. A Finite Element Model (FEM) was proposed to assess the temperature and thermomechanical stress in work roll during hot rolling. An analytical method was developed to implement the three initial residual stress components designed by the full factorial analysis of variance (ANOVA) method in the obtained FEM thermomechanical stress results. An Artificial Neural Network (ANN) was used to establish an objective function to relate the initial residual stress components to the MVMS. Subsequently, the single and multi-objective Genetic Algorithm (GA) optimization were used to find the optimal value of initial residual stress components to minimize the MVMS on the surface and subsurface of the work roll. The results showed a significant reduction of both
the value and amplitude of the MVMS on surface and subsurface of a work roll during the hot rolling process. 
کلیدواژه‌ها
Residual stress؛ Von-Mises stress؛ Design of experiment؛ Artificial neural network؛ Genetic algorithm؛ Optimization؛ Hot rolling
مراجع

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