ArticleSensors (Basel, Switzerland)2016
Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Research on Tool Wear Prediction Method Based on CNN-ResNet-CBAM-BiGRU.Sensors (Basel, Switzerland) · 2026Article
- A new open dataset from a milling process - data for classification and estimation of tool life.Scientific data · 2025Article
- Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders.Sensors (Basel, Switzerland) · 2024Article
- Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis.Sensors (Basel, Switzerland) · 2024Article
- Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review.Sensors (Basel, Switzerland) · 2024Review
- Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection.Sensors (Basel, Switzerland) · 2023Article
- Tool Condition Monitoring for High-Performance Machining Systems-A Review.Sensors (Basel, Switzerland) · 2022Review
- Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders.Sensors (Basel, Switzerland) · 2021Article
- A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction.Sensors (Basel, Switzerland) · 2020Article
- A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction.Sensors (Basel, Switzerland) · 2020Article
- Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations.Sensors (Basel, Switzerland) · 2020Article
- Proactive Scheduling for Job-Shop Based on Abnormal Event Monitoring of Workpieces and Remaining Useful Life Prediction of Tools in Wisdom Manufacturing Workshop.Sensors (Basel, Switzerland) · 2019Article
- Machined Surface Quality Monitoring Using a Wireless Sensory Tool Holder in the Machining Process.Sensors (Basel, Switzerland) · 2019Article
- Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1-PZT Diaphragm Transducer Response and EMI Sensing Technique.Sensors (Basel, Switzerland) · 2018Article
- A Multisensor Fusion Method for Tool Condition Monitoring in Milling.Sensors (Basel, Switzerland) · 2018Article
- Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.
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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.