标题:Specific energy consumption prediction model of CNC machine tools based on tool wear
作者:Zhao, Guoyong; Li, Chunxiao; Lv, Zhe; Cheng, Xiang; Zheng, Guangming
作者机构:[Zhao, Guoyong; Li, Chunxiao; Lv, Zhe; Cheng, Xiang; Zheng, Guangming] Shandong Univ Technol, Inst Adv Mfg, Zibo, Peoples R China.
通讯作者:Zhao, Guoyong;Zhao, GY
通讯作者地址:[Zhao, GY]Shandong Univ Technol, Inst Adv Mfg, Zibo, Peoples R China.
来源:INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
出版年:2020
卷:33
期:2
页码:159-168
DOI:10.1080/0951192X.2020.1718763
关键词:Processing parameters; tool wear; cutting power; specific energy; consumption; prediction accuracy
摘要:The global energy crisis and climate warming are becoming more and more serious. How to reduce energy consumption and environmental pollution to achieve low-carbon manufacturing is an urgent problem to solve. Tool wear leads to the increase of cutting force and cutting power of machine tools obviously. So the influence of tool wear on energy consumption of machine tools cannot be ignored. Firstly, the power of CNC machine tool in cutting stage is divided into standby power, cutting material power and spindle no-load power in the paper. Then, the specific energy consumption prediction model of CNC machine tools based on tool wear is developed. Furthermore, the proposed model is verified with dry milling 45# steel experiments, and the prediction accuracy can reach 98.2% according to material removal rate, spindle speed and tool wear. Finally, the influence of processing parameters and tool wear on specific energy consumption of machine tools is studied. The research is helpful to optimize the processing parameters and tool conditions to reduce the energy consumption of machine tools.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078464306&doi=10.1080%2f0951192X.2020.1718763&partnerID=40&md5=8c98db9a58151cf3f03c2e7459c91fd3
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