Novel Method in Induction Heating for Complex Steel Plate Deformation Based on Artificial Neural Network

Nguyen Dao Xuan Hai, Nguyen Truong Thinh and Wen-Long Shang

Complexity, 2022, vol. 2022, 1-14

Abstract: The implementation of an artificial neural network for predicting induction heating region locations is proposed in this research. Steel plate deformations during the induction heating process are produced using an analytical solution derived from electromagnetic and plate theory. The plate transform following vertical displacements in each divided area was used as input of neural following desired shape of the steel plate and the specified heating areas for induction treatment as output parameters to predict and evaluate the model. A dataset used 90% for training and remaining 10% for testing to implement on the efficient models when changing hidden layer and its neurons relatively. The trial and error for analyzing and predicting heating-affected regions with the ANNs model reached the high average accuracy and lowest mean square error at 98.08% and 0.00913, respectively. Consequently, the feasibility test indicates that the developed approach may be well utilized to identify the heating positions by grid area in order to achieve the desired plate deformation. Moreover, the analysis of vertical displacement during induction heating and its response behaviour of steel plate based on thermo-mechanical are also addressed.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3531980

DOI: 10.1155/2022/3531980

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