Maximum ladle shell temperature prediction based on GABP neural network Online publication date: Wed, 19-Apr-2023
by Ying Sun; Peng Huang; Bo Tao; Juntong Yun; Guojun Zhao; Xin Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 24, No. 2, 2023
Abstract: Intelligent manufacturing is the main development trend of today's manufacturing industry, and talents are the first resource. Through the analysis of the current situation of cultivating talents in mechanical engineering in colleges and universities, it is found that most students of this major have difficulty in involving in knowledge of other fields outside their specialties, and their knowledge structure is relatively single. In response to the above problems, this paper proposes the training mode of multidisciplinary cross-integration of talents in mechanical engineering, which is analysed through the study of maximum temperature prediction of steel ladle shell. The BP neural network based on the improved genetic algorithm is trained on the experimental data samples to achieve the maximum temperature prediction of ladle shell under different thickness combinations of insulation layer, safety layer and working layer. By learning the knowledge of target prediction, students' overall development is promoted.
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