Chooseing  Grinding Parameters Intelligently Based on the Artificial Neural Networks

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Chooseing Grinding Parameters Intelligently Based on the Artificial Neural Networks

更新时间：2023-11-28

- Chooseing Grinding Parameters Intelligently Based on the Artificial Neural Networks
- Advanced Engineering Sciences Vol. 40, Issue 4, Pages: 176-180(2008)
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- Published：2008，
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罗红波,孟令锋,唐才学.基于神经网络的磨削参数智能选择[J].工程科学与技术,2008,40(4):176-180.

Chooseing Grinding Parameters Intelligently Based on the Artificial Neural Networks. [J]. Advanced Engineering Sciences 40(4):176-180(2008)
罗红波,孟令锋,唐才学.基于神经网络的磨削参数智能选择[J].工程科学与技术,2008,40(4):176-180. DOI：

Chooseing Grinding Parameters Intelligently Based on the Artificial Neural Networks. [J]. Advanced Engineering Sciences 40(4):176-180(2008) DOI：

摘要

中文摘要: 在用人工神经网络来确定磨削参数的过程中，在传统 BP算法的基础上，采用改进了的GCAQBP算法。同时考虑了砂轮材料和粒度对磨削过程的影响，把它们增加为神经网络系统的输入参数；并对输入参数的编码进行了细化，最终建立了磨削参数智能选择系统。用样本进行训练后有效率达到了80%以上，与传统方法相比提高了磨削加工的效率和精度。

Abstract

Abstract:In the process of choosing grinding parameters using the method of the artificial neural networks

an improved GCAQBP algorithm on the basis of the BP algorithm was used to establish the mathematical model. The material and granularity of the grinding wheel were considered as the input parameters of the artificial neural networks

and the encoding of the input parameters was refined. Test results showed that the correct rate was more than 80 percent

compared with the traditional method.

关键词

磨削参数GCAQBP算法神经网络

Keywords

grinding parametersGCAQBP algorithmNeural network

references

Radhakrishnan,Nandan,Milling force prediction using regression and neural networks,Journal of Intelligent Manufacturing,2006(1).

刘贵杰.巩亚东.王宛山磨削加工参数智能化在线调整方法研究 [J].

Egon Susic,Igor Grabec,Characterization of the grinding process by acoustic emission,International Journal of Machine Tools and Manufacture,2000(2).

马尔金,磨削技术理论与应用,沈阳:东北大学出版社,2002.

李伯民,赵波,现代磨削技术,北京:机械工业出版社,2003.

王兆宇.袁赣南.邱威基于神经网络的全局寻优自适应BP学习算法 [J].

王一晶.左志强基于改进BP网络的广义预测控制快速算法 [J].

谢庆生,罗延科,机械工程中的神经网络方法,北京:机械工业出版社,2003.

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