ReviewSensors (Basel, Switzerland)2020
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 278 citations in OpenAlex.
- 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
- Sound-Based Tool Wear Classification in Turning of AISI 316L Using Multidomain Acoustic Features and SHAP-Enhanced Gradient Boosting Models.Materials (Basel, Switzerland) · 2026Article
- AI based decision-making system for tooling design of aircraft product assembly with developed knowledge retrieval algorithm.Scientific reports · 2026Article
- Advanced Sensor Technologies in Cutting Applications: A Review.Sensors (Basel, Switzerland) · 2026Review
- Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification.Scientific reports · 2025Article
- Review of advanced sensor system applications in grinding operations.Journal of advanced research · 2025Review
- A Hybrid Deep Learning Framework for Fault Diagnosis in Milling Machines.Sensors (Basel, Switzerland) · 2025Article
- Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing.Sensors (Basel, Switzerland) · 2025Article
- The Influence of the Machining Parameters of AW-7020 Aluminum Alloy Shafts on the Surface Roughness, Cutting Forces, and Acoustic Emission Signal.Materials (Basel, Switzerland) · 2025Article
- Trend analysis and prediction of fabric tear performance testing processes based on the BLTT-FT model.PloS one · 2025Article
- Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- An Automated Feature-Based Image Registration Strategy for Tool Condition Monitoring in CNC Machine Applications.Sensors (Basel, Switzerland) · 2024Article
- A smart look at monitoring while drilling (MWD) and optimizing using acoustic emission technique (AET).Scientific reports · 2024Article
- Acoustic Features of the Impact of Laser Pulses on Metal-Ceramic Carbide Alloy Surface.Sensors (Basel, Switzerland) · 2024Article
- Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.Sensors (Basel, Switzerland) · 2024Article
- Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network.Sensors (Basel, Switzerland) · 2024Article
- Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.Sensors (Basel, Switzerland) · 2024Article
- Multi-Objective Optimization of Machining Parameters for Drilling LM5/ZrOMaterials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.
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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.