Danil Yu. Pimenov, Leonardo Rosa Ribeiro da Silva, Mustafa Kuntoğlu, Bruno Souza Abrão, Luiz Eduardo dos Santos Paes, Emanoil Linul
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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":"{\"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. 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Review of advanced sensor system applications in grinding operations
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.
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.