Review of advanced sensor system applications in grinding operations

IF 11.4 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Danil Yu. Pimenov, Leonardo Rosa Ribeiro da Silva, Mustafa Kuntoğlu, Bruno Souza Abrão, Luiz Eduardo dos Santos Paes, Emanoil Linul
{"title":"Review of advanced sensor system applications in grinding operations","authors":"Danil Yu. Pimenov, Leonardo Rosa Ribeiro da Silva, Mustafa Kuntoğlu, Bruno Souza Abrão, Luiz Eduardo dos Santos Paes, Emanoil Linul","doi":"10.1016/j.jare.2025.01.013","DOIUrl":null,"url":null,"abstract":"<h3>Background</h3>Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality.<h3>Aim of Review</h3>The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations.<em>Key Scientific Concepts of Review:</em> The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"2020 1","pages":""},"PeriodicalIF":11.4000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1016/j.jare.2025.01.013","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality.

Aim of Review

The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations.Key Scientific Concepts of Review: The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.

Abstract Image

先进传感器系统在磨削作业中的应用综述
今天,在各种各样的行业中,磨削操作是获得精确尺寸和满足严格的粗糙度和形状精度要求的极其重要的精加工过程。然而,磨具在磨削过程中的持续磨损会对工件的尺寸和表面状况产生负面影响。因此,有效监测磨削过程中的磨损过程有助于预测刀具寿命,计划维护并确保一致的产品质量。本综述的目的是研究当前各种传感器系统中直接和间接的刀具状态监测技术,以及它们在传统和人工智能驱动的磨削过程中的应用。通过对这些技术的研究,该综述深入了解了不同的监控技术如何提高工艺效率,减少停机时间,提高成品质量,以及智能和自适应工艺在传统磨削操作中的应用。综述的关键科学概念:综述讨论了传感器系统在监测工具状态中的关键作用,包括成像、振动分析、声发射和力测量等技术。这些系统对于检测磨损和预测故障、及时干预和防止意外停机至关重要。将人工智能集成到这些监测系统中,大大提高了它们的能力,因为它们能够实现更主动的策略,并适应磨削过程中不断变化的条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Advanced Research
Journal of Advanced Research Multidisciplinary-Multidisciplinary
CiteScore
21.60
自引率
0.90%
发文量
280
审稿时长
12 weeks
期刊介绍: Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences. The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信