Evidence map›Paper›PMID 39798850›Full record

ReviewJournal of advanced research2025

Review of advanced sensor system applications in grinding operations.

Danil Yu Pimenov, Leonardo Rosa Ribeiro da Silva, Mustafa Kuntoğlu, Bruno Souza Abrão, Luiz Eduardo Dos Santos Paes, Emanoil Linul

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Danil Yu PimenovDepartment of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080 Russia. Electronic address: danil_u@rambler.ru.
Leonardo Rosa Ribeiro da SilvaFaculty of Mechanical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, MG, Brazil.
Mustafa KuntoğluSelcuk University, Technology Faculty, Mechanical Engineering Department, Konya 42130, Turkey.
Bruno Souza AbrãoFaculty of Mechanical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, MG, Brazil.
Luiz Eduardo Dos Santos PaesFaculty of Mechanical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, MG, Brazil.
Emanoil LinulDepartment of Mechanics and Strength of Materials, Politehnica University Timisoara, 1 Mihai Viteazu Avenue, 300 222 Timisoara, Romania. Electronic address: emanoil.linul@upt.ro.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundToday, 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.

Indexed as

Conventional and artificial intelligence applicationsGrindingMachiningSensor systemsTool condition monitoring

Identifiers

PMID39798850
PMCPMC12627894

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.