Paper Title:
Parameter Optimization of Laser Scribing Technics of 30Q130 Grain-Oriented Silicon Steel Based on Genetic Neural Network
  Abstract

A laser is often considered to scribe the grain-oriented silicon steel surfaces after cold-rolling and annealing to reduce the core loss. It is necessary to select the best scribing parameters to maximize the reduction in this process. This paper proposed an optimization method of genetic algorithm during laser scribing of 30Q130 steel, by developing an artificial neural network prediction model using a database form a designed orthogonal experiment. The objective was to determine the best combination values of three important scribing parameters, namely scribing velocity, pulse energy and scanning spacing, that can get the largest core loss reduction. An optimized combination of parameters was obtained by this method and then validated by an adding experiment. The result indicates that the optimization model is reliable.

  Info
Periodical
Edited by
Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng
Pages
844-848
DOI
10.4028/www.scientific.net/AMM.37-38.844
Citation
Y. Huang, Y. Liu, G. L. Zhu, "Parameter Optimization of Laser Scribing Technics of 30Q130 Grain-Oriented Silicon Steel Based on Genetic Neural Network", Applied Mechanics and Materials, Vols. 37-38, pp. 844-848, 2010
Online since
November 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Wei Wang, Jian Shi Yao, Guang Qi Cai
Abstract:In the grinding process of special spiral rods, the actual feed rate is far below the allowable limit, resulting in great efficiency waste....
118
Authors: Bing Kun Zhu, Li Hong Xu, Hai Gen Hu
Abstract:Multi-objective optimization is a challenging research topic because it involves the simultaneous optimization of several complex and...
818
Authors: Tian Zhong Sui, Lei Wang, Dong Mei Cheng, Hong Wen Cui
Abstract:In this paper, a multi-objective parameter optimization model based on experimental design and NN-GA is established. In this method,...
12
Authors: Si Lian Xie, Tie Bin Wu, Shui Ping Wu, Yun Lian Liu
Chapter 18: Computer Applications in Industry and Engineering
Abstract:Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional...
2846
Authors: Jian Xue Chen, Shui Yu
Chapter 4: Mechatronics and Automation Manufacturing Systems, Control Technologies
Abstract:Combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, the ACO-BP algorithm is proposed to optimize shift...
553