应用人工神经网络预测车削过程中的切削力

M. Ibraheem
{"title":"应用人工神经网络预测车削过程中的切削力","authors":"M. Ibraheem","doi":"10.22153/kej.2020.04.002","DOIUrl":null,"url":null,"abstract":"Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.","PeriodicalId":7637,"journal":{"name":"Al-Khwarizmi Engineering Journal","volume":"45 1","pages":"34-46"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Prediction of Cutting Force in Turning Process by Using Artificial Neural Network\",\"authors\":\"M. Ibraheem\",\"doi\":\"10.22153/kej.2020.04.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.\",\"PeriodicalId\":7637,\"journal\":{\"name\":\"Al-Khwarizmi Engineering Journal\",\"volume\":\"45 1\",\"pages\":\"34-46\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Al-Khwarizmi Engineering Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22153/kej.2020.04.002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Al-Khwarizmi Engineering Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22153/kej.2020.04.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

切削力是决定机床使用性能和产品质量的重要因素。在车削加工中,进给速度、切削深度和刀具噪声半径等因素影响着表面粗糙度和切削力。采用人工神经网络模型对切削速度(m/min)、进给速度(mm/rev)、切削深度(mm)和工件硬度(Map)等输入进行切削力预测。该神经网络模型的输出为加工后的切削力参数,神经网络显示出加工力各分量的全部(输出)切削力FT (N)、进给力FA (N)和径向力FR (N)与实验数据完全吻合。使用了25个实验数据样本,其中19个用于训练网络。此外,还进行了6个实验测试来测试该网络。研究表明,神经网络是一种可靠、精确的数控车削加工参数预测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
GSM-enabled Wireless Patient Monitoring System Integrating Microcontroller for Managing Vital Signstal signs Performance Analysis of Different Machine Learning Algorithms for Predictive Maintenance Modeling and Simulation of Hydraulic Proportional Control Valves with Different Types of Controllers s Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup Assessment of Foot Deformities in Patient with Knee Osteoarthritis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1