{"title":"机器学习与声发射信号特征相结合,用于估计铬镍铁合金 718 超声波加工过程中的刀具磨损情况","authors":"Mehdi Mehtab Mirad, Bipul Das","doi":"10.1080/10910344.2023.2299443","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":51109,"journal":{"name":"Machining Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning coupled with acoustic emission signal features for tool wear estimation during ultrasonic machining of Inconel 718\",\"authors\":\"Mehdi Mehtab Mirad, Bipul Das\",\"doi\":\"10.1080/10910344.2023.2299443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":51109,\"journal\":{\"name\":\"Machining Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machining Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10910344.2023.2299443\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machining Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10910344.2023.2299443","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
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