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Hi-MDTCN: Hierarchical Multi-Scale Dilated Temporal Convolutional Network for Tool Condition Monitoring.

Anying Chai¹, Zhaobo Fang¹, Mengjia Lian²

¹College of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces Hi-MDTCN, a novel deep learning model for accurate tool wear identification. The Hierarchical Multi-scale Dilated Temporal Convolutional Network effectively analyzes sensor data, improving tool condition recognition.

Keywords:

deep learning multi-modal multi-sensor fusion tool condition monitoring

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Area of Science:

Manufacturing Engineering
Artificial Intelligence
Signal Processing

Background:

Single-sensor data offers limited insights into tool wear.
Existing multi-sensor fusion methods struggle with data complementarity and redundancy.

Purpose of the Study:

To develop an advanced deep learning model for precise tool wear condition identification.
To overcome limitations in current single-sensor and multi-sensor fusion techniques.

Main Methods:

Proposed Hi-MDTCN (Hierarchical Multi-scale Dilated Temporal Convolutional Network) with a hierarchical signal analysis framework.
Utilized a Multi-channel 1D CNN with attention for intra-segment feature extraction.
Employed a Bi-TCN module for inter-segment long-term dependency modeling.
Leveraged dilated convolution for large receptive fields and efficient parallel processing.

Main Results:

Hi-MDTCN demonstrated superior tool condition recognition accuracy on the PHM2010 milling dataset.
The model effectively captured local wear features and long-term wear evolution trends.
Achieved efficient parallel capture of long-range dependencies, outperforming traditional methods.

Conclusions:

The proposed Hi-MDTCN model offers an effective solution for accurate tool wear identification.
Hi-MDTCN enhances tool life, processing quality, and production efficiency.
The method shows significant potential for practical applications in industrial settings.