[1]吴瑞尊,徐西鹏,何江川.基于BP神经网络的金刚石锯片寿命预测[J].华侨大学学报(自然科学版),2000,21(1):57-60.[doi:10.3969/j.issn.1000-5013.2000.01.012]
　Wu Ruizun,Xu Xipeng,He Jiangchuan.A Method Based on BP Neural Network for Predicting the Life of Diamond Saw-blade during Granite Sawing[J].Journal of Huaqiao University(Natural Science),2000,21(1):57-60.[doi:10.3969/j.issn.1000-5013.2000.01.012]
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基于BP神经网络的金刚石锯片寿命预测()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第21卷
期数:
2000年第1期
页码:
57-60
栏目:
出版日期:
2000-01-20

文章信息/Info

Title:
A Method Based on BP Neural Network for Predicting the Life of Diamond Saw-blade during Granite Sawing
文章编号:
1000-5013(2000)01-0057-04
作者:
吴瑞尊; 徐西鹏; 何江川
华侨大学机电工程系, 泉州362011
Author(s):
Wu Ruizun;  Xu Xipeng;  He Jiangchuan
Dept. of Electromech. Eng., Huaqiao Univ., 362011, Quanzhou
关键词:
BP神经网络;  金刚石锯片;  花岗石;  寿命;  预测
Keywords:
BP neural network;  diamond saw blade;  granite;  life;  prediction
分类号:
TP18
DOI:
10.3969/j.issn.1000-5013.2000.01.012
摘要:
在花岗石锯切过程中影响金刚石锯片寿命的因素交错复杂,对正确设计、制造和使用工具都带来极大困难 .利用神经网络方法,研究花岗石种类、特性、锯切参数等多种因素对工具寿命的交互影响规律,对花岗石锯切用金刚石圆锯片的制造、使用起了指导作用,从而提高工具的加工效率,降低加工成本 .经网络训练表明 :(1)以花岗石的石英、长石和斜长石的百分含量、抗拉强度、抗压强度; (2 )以锯切用量的切深、进给量、锯片圆周速度为输入量; (3)以锯片的相对寿命为输出量的 3层 7- 15- 1结构的 BP神经网络,这三者的有机配合能有效地预测锯片的寿命 .
Abstract:
During granite sawing, factors influencing the life of diamond saw blade are complicated which will offer difficulty to the proper design, manufacture and service of the tool. By applying the method of neural network, a study is devoted to the effects of variety and characteristic and parameters of sawing of granite on the life of the tool. The study will serve as a guide to the manufacture and the service of diamond saw blade for granite sawing and thus will increase machining effciency of the tool and will decrease its cost. As shown by large amount of training, with percent content of guartz and feldspar and plagioclase in granite, tensile strength and compressive strength of granite, depth of cut, feed, and peripheral speed of saw blade as inputs and with relative life of saw blade as output, the life of saw blade can be effectively predicted by BP neural network of three layered 7 15 1 structure.

参考文献/References:

[1] 徐西鹏, 沈剑云, 黄辉. 实现花岗石高效锯切的关键因素分析 [J]. 机械工程学报, 1998(1):104-110.doi:10.3321/j.issn:0577-6686.1998.01.018.
[2] 胡守仁, 余少波, 戴葵. 神经网络导论 [M]. 北京:国防科技出版社, 1997.1-176.
[3] 方柏山. 谷氨酸发酵调优操作模型 [J]. 华侨大学学报(自然科学版), 1994(3):331-335.

备注/Memo

备注/Memo:
福建省自然科学基金资助项目
更新日期/Last Update: 2014-03-23