Research of Self-Learning of Plate Deformation Resistance Based on Genetic Algorithm

Chun Yu He; Zhi Jie Jiao; Di Wu

doi:10.4028/www.scientific.net/AMR.154-155.260

Paper Titles

Theoretical Analysis of Serrated Chip Formation Based on Ideal Models in High Speed Cutting
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Research on Multi-Domain Unified Modeling and Optimization of a Micro Mirror
p.246

Improved Online Prediction of Silicon Content in Ironmaking Process Using Support Vector Regression with Novel Outlier Detection
p.251

Investigation on Microstructure and Property of Non-Electric Welding Joint
p.256

Research of Self-Learning of Plate Deformation Resistance Based on Genetic Algorithm
p.260

Study of Influence of Stray Current on Potential Distribution of Pipeline by Numerical Simulation
p.265

A Segregative MC Carbide in Centrifugal Casting High Speed Steel Roll
p.269

Research on the High-Efficiency and Energy-Saving Cutting of the Aero Engine Fan Blades
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Analyses of Guiding Force for the Ring Rolling Mill
p.278

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 154-155Research of Self-Learning of Plate Deformation...

Research of Self-Learning of Plate Deformation Resistance Based on Genetic Algorithm

Abstract:

The model parameters value of deformation resistance determines the prediction accuracy of rolling force model during the plate rolling. According to the influencing factors analysis of rolling force calculation error, the genetic algorithm was introduced into the self-learning method of deformation resistance, and searches the optimal value of deformation resistance on the basic of space exploration and optimization ability of genetic algorithm. The decision variable selection, the coding and decoding, the fitness evaluation and the terminal conditions process were implemented during development process of self-learning system. The results show that the optimization speed and accuracy can meet production requirement.