ArticleSensors (Basel, Switzerland)2018
A Multisensor Fusion Method for Tool Condition Monitoring in Milling.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Cross-Condition Tool Wear State Monitoring via Multi-Source Sensor Signal Fusion and Supervised Transfer Learning.Sensors (Basel, Switzerland) · 2026Article
- Research on Tool Wear Prediction Method Based on CNN-ResNet-CBAM-BiGRU.Sensors (Basel, Switzerland) · 2026Article
- An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.Sensors (Basel, Switzerland) · 2025Article
- Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
- On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet.Sensors (Basel, Switzerland) · 2022Article
- Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing.Sensors (Basel, Switzerland) · 2021Article
- A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.Sensors (Basel, Switzerland) · 2021Article
- Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process.Sensors (Basel, Switzerland) · 2021Article
- Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling.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
- Streaming Data Fusion for the Internet of Things.Sensors (Basel, Switzerland) · 2019Article
- A Mission Reliability-Driven Manufacturing System Health State Evaluation Method Based on Fusion of Operational Data.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
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2 authors.
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Abstract
Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time⁻frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson's correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.
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