Prediction of influence parameters on the hot rolling process using finite element method and neural network

Shahani, A.R.; Setayeshi, Saeid; Nodamaie, S.A.; Asadi, Mohammad-Ali; Rezaie, S.

doi:10.1016/j.jmatprotec.2008.04.055

Cited by 117 publications

(44 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surface hardness of the material is assumed to be approximately 3 times the yield stress of the bulk material [9], and the thermal conductivities of metal material 10 and lubricant are set as and respectively based on the parameters used in [42]. The computed result and comparison with previous research are shown in Fig.…”

Section: Contact Conductance In the Wzmentioning

confidence: 99%

Interfacial heat transfer under mixed lubrication condition for metal rolling: A theoretical calculation study

Liang

Tang

et al. 2016

Applied Thermal Engineering

View full text Add to dashboard Cite

Section: Contact Conductance In the Wzmentioning

confidence: 99%

Interfacial heat transfer under mixed lubrication condition for metal rolling: A theoretical calculation study

Liang

Tang

et al. 2016

Applied Thermal Engineering

View full text Add to dashboard Cite

“…The temperature distribution in the plate can be calculated by using the governing partial differential equation of heat transfer [12]:…”

Section: Thermo-mechanical Coupled Modelmentioning

confidence: 99%

Finite Element Analysis of Cross Rolling on AISI 304 Stainless Steel: Prediction of Stress and Strain Fields

Rout

Pal

Singh

2016

J. Inst. Eng. India Ser. C

View full text Add to dashboard Cite

Studies on the effect of strain path during rolling has been carried out for a long time, but the same has not been done using Finite Element Analysis (FEA). Change in strain path affects the state variables in the rolled plate like stress, strain, temperature etc. In the current work, Finite Element Analysis for cross rolling of AISI 304 austenitic stainless steel has been carried out by rotating the plate by 90°in between the passes. To analyze stress and strain fields in the material for cross rolling, a full 3D model of work-roll and plate has been developed using rigid-viscoplastic finite element method. The stress and strain fields, considering von-Mises yield criteria, are calculated by using updated Lagrangian method. In addition to these, the model also calculates the normal pressure and strain rate distribution in the plate during cross rolling. The nature of the variations of stress and strain fields in the plate, predicted by the model, is in good agreement with the previously published works for unidirectional rolling.

show abstract

“…The parameters such as the geometry of slab, thickness reduction, rolling speed, frictional coefficient and load are used in developing Artifical Neural Network (ANN) using the output of FE simulation by training the network. This network predicts the workpiece behavior during the rolling process [11]. In the present work, the effect of rolling parameters and optimisation of AA1100 for the billet of size 40mm  40mm  100mm length is considered.…”

Section: Introductionmentioning

confidence: 99%

Optimize the Rolling Process Parameters for Material AA1100 using Metal Forming Simulation

2018

View full text Add to dashboard Cite

Abstract. Metal forming plays a very important role in the manufacturing. Simulation of manufacturing process aids in the improvement of quality, reduce energy and resource consumption and helps in visualization of the process. The design of experiment helps in optimization of the parameters in any processes. In this paper, Taguchi optimization technique is used to predict the best results for the given inputs such as roller diameter, friction value, velocity of the rollers and percentage reduction to the forming process and get the optimized values for spread, hardness, effective stress, power required, strain rate and torque using the manufacturing simulation software. It is found that the important parameter is percentage reduction affecting the effective stress. Optimal parameters with desirability value of 0.87 have been obtained.

show abstract

Prediction of influence parameters on the hot rolling process using finite element method and neural network

Cited by 117 publications

References 22 publications

Interfacial heat transfer under mixed lubrication condition for metal rolling: A theoretical calculation study

Interfacial heat transfer under mixed lubrication condition for metal rolling: A theoretical calculation study

Finite Element Analysis of Cross Rolling on AISI 304 Stainless Steel: Prediction of Stress and Strain Fields

Optimize the Rolling Process Parameters for Material AA1100 using Metal Forming Simulation

Contact Info

Product

Resources

About