Optimization of Fixed-abrasive Tool Development Parameters Based on BP Neural Network and Genetic Algorithm

被引：0
|
作者：
Zhang X. [1 ]
Wang Y.-G. [1 ]
Chen H.-Y. [1 ]
Hang W. [1 ]
Cao L.-L. [2 ]
Deng H. [3 ]
Yuan J.-L. [1 ]
机构：
[1] Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology of Ministry of Education, Zhejiang University of Technology, Hangzhou
[2] Mechanical Engineering, Beihua University, Jilin
[3] College of Engineering, Southern University of Science and Technology, Shenzhen
来源：
Surface Technology | 2022年 / 51卷 / 02期
关键词：
BP neural network; fixed-abrasive tool; genetic algorithm; orthogonal experiment; sapphire;
D O I：
10.16490/j.cnki.issn.1001-3660.2022.02.036
中图分类号：
学科分类号：
摘要：
The work aims to find the optimal combination of fixed-abrasive tool pellet development parameters, and achieve fast optimization of development parameters for fixed-abrasive tool pellets. The Cr2O3 pellet was used to test on the sapphire workpiece in polishing machine. The pellet was developed according to the orthogonal parameter scheme, which was designed according to particle size, mass fraction, molding pressure, and sintering temperature. And designed the experiment to test the hardness and compressive strength of the fixed-abrasive tool pellets which were developed according to the different sintering temperature. The test results verified the validity of the self-made fixed-abrasive tool pellets and the rationality of sintering temperature selection. The removal rate of sapphire wafers and the grinding ratio of Cr2O3 pellets were measured. Considering the grinding efficiency and economy of the pellet, the removal rate and grinding ratio were normalized by min-max method, and the weight values were multiplied by corresponding weight and added together to obtain the comprehensive score Y, which was used as the evaluation standard of the pellet. With particle size, mass fraction, molding pressure, sintering temperature as input values and comprehensive score Y as output values, a prediction model of pellet comprehensive score Y based on neural network was established. The training results of the BP neural network was evaluated by the coefficient of determination R2. Designed the orthogonal parameter scheme of initial population N, crossover probability Pc and mutation probability Pm . According to the orthogonal parameter scheme, genetic algorithm was used to optimize the global process parameters based on the BP neural network. The genetic algorithm was used to optimize the global manufacturing process parameters According to the optimization results, the pellet is developed and tested on the polishing machine. Then calculated comprehensive score and compared such score with the neural network prediction score. A three-layer BP neural network with 4 input layer neurons, 12 hidden layer neurons and 1 output layer neuron was constructed. The determined coefficient R2 of the constructed BP neural network is 0.9313, and the error between the predicted value of the comprehensive score Y and the actual value is less than 4%, which could meet the practical application of the project. Within the given range of development parameters, under the condition that the genetic algorithm parameter combination is the initial population individual N is 80, crossover probability Pc is 0.6, mutation probability Pm is 0.06, the optimal manufacturing development parameter combination of Cr2O3 fixed-abrasive tool for sapphire polishing obtained by genetic algorithm optimization is: abrasive grain size 10 μm, abrasive grain mass fraction 88%, molding pressure 80 MPa, molding temperature 174 ℃, the optimal value of the comprehensive score Y of pellet is 94.09. The average comprehensive score obtained by the experiment is 89.87, and the error is 5% compared with the optimal value. BP neural network can effectively establish a prediction model between the development parameters and processing quality of the abrasive-fixed tool pellets. Neural network combined with genetic algorithm optimization can provide guiding significance for the optimal selection of the development parameter combination of abrasive-fixed tool. © 2022, Chongqing Wujiu Periodicals Press. All rights reserved.
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页码：358 / 366
页数：8
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