Research on tool wear prediction based on the random forest optimized by NGO algorithm

IF 2.7 4区 工程技术 Q2 ENGINEERING, MANUFACTURING Machining Science and Technology Pub Date : 2024-06-07 DOI:10.1080/10910344.2024.2359928
Yaonan Cheng, Shilong Zhou, Jing Xue, M. Lu, Xiaoyu Gai, R. Guan
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基于 NGO 算法优化的随机森林的刀具磨损预测研究
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来源期刊
Machining Science and Technology
Machining Science and Technology 工程技术-材料科学:综合
CiteScore
5.70
自引率
3.70%
发文量
18
审稿时长
6 months
期刊介绍: Machining Science and Technology publishes original scientific and technical papers and review articles on topics related to traditional and nontraditional machining processes performed on all materials—metals and advanced alloys, polymers, ceramics, composites, and biomaterials. Topics covered include: -machining performance of all materials, including lightweight materials- coated and special cutting tools: design and machining performance evaluation- predictive models for machining performance and optimization, including machining dynamics- measurement and analysis of machined surfaces- sustainable machining: dry, near-dry, or Minimum Quantity Lubrication (MQL) and cryogenic machining processes precision and micro/nano machining- design and implementation of in-process sensors for monitoring and control of machining performance- surface integrity in machining processes, including detection and characterization of machining damage- new and advanced abrasive machining processes: design and performance analysis- cutting fluids and special coolants/lubricants- nontraditional and hybrid machining processes, including EDM, ECM, laser and plasma-assisted machining, waterjet and abrasive waterjet machining
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