ReviewSensors (Basel, Switzerland)2022
Tool Condition Monitoring for High-Performance Machining Systems-A Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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.
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Who cites it
17 citing papers in PubMed.
- Editorial for the Special Issue on Advanced Micro- and Nano-Manufacturing Technologies, 2nd Edition.Micromachines · 2026Article
- Multi-Physics Monotone Score Transport for Unsupervised Domain Adaptation of Continuous Tool Wear Prediction.Sensors (Basel, Switzerland) · 2026Article
- AI-Based Predictive Maintenance Framework for Industrial Saw Blade Wear Monitoring Using Low-Cost Vibration Sensors.Sensors (Basel, Switzerland) · 2026Article
- IIoT-Based Remote Monitoring System for Temperature, Current, and Vibration Using PLC and Node-RED in a Data Center Cooling Compressor: A Condition-Based Maintenance Framework.Sensors (Basel, Switzerland) · 2026Article
- Advanced Sensor Technologies in Cutting Applications: A Review.Sensors (Basel, Switzerland) · 2026Review
- In production system for tool wear prediction using multi-sensor time series and machine learning models.Discover applied sciences · 2026Article
- Hi-MDTCN: Hierarchical Multi-Scale Dilated Temporal Convolutional Network for Tool Condition Monitoring.Sensors (Basel, Switzerland) · 2025Article
- An Automated Feature-Based Image Registration Strategy for Tool Condition Monitoring in CNC Machine Applications.Sensors (Basel, Switzerland) · 2024Article
- Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling.Sensors (Basel, Switzerland) · 2024Article
- Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard.Sensors (Basel, Switzerland) · 2024Article
- Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization.Sensors (Basel, Switzerland) · 2023Article
- Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes.Sensors (Basel, Switzerland) · 2023Article
- Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model.Sensors (Basel, Switzerland) · 2023Article
- Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings.Micromachines · 2023Article
- Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework.Sensors (Basel, Switzerland) · 2022Article
- Design and Evaluation of Low-Cost Vibration-Based Machine Monitoring System for Hay Rotary Tedder.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of the "Industry 4.0" revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems' generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.
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Registered trials
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.