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Related Concept Videos

Mechanical Systems01:22

Mechanical Systems

Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically described...
Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

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Related Experiment Video

Updated: Jun 27, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

PHM Services Based on Cyber-Physical Machine Tool System.

Chuting Wang1,2, Ruijuan Xue1, Xuesong Mei2

  • 1General Technology Key Laboratory of High-End CNC Machine Tools, Beijing 100102, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a digital twin framework for machine tool Prognostics and Health Management (PHM), overcoming data silos with real-time synchronization and a novel ResNet-TCN diagnostic model for accurate tool wear prediction.

Keywords:
CNC machine toolPHMdigital twin

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Area of Science:

  • Manufacturing Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Heterogeneous fault data and poor real-time synchronization in CNC machine tools impede effective Prognostics and Health Management (PHM).
  • Existing systems often suffer from information silos, limiting diagnostic accuracy and responsiveness.

Purpose of the Study:

  • To design and implement a digital twin-driven PHM framework for machine tools.
  • To address challenges of data heterogeneity and real-time synchronization in CNC machine tool monitoring.
  • To develop an accurate and scalable PHM solution for smart manufacturing.

Main Methods:

  • Development of a unified machine-tool fault information dictionary.
  • Implementation of a mechanism-data dual-driven diagnostic model (ResNet-TCN).
  • Creation of a cyber-physical platform using OPC UA and RESTful APIs for real-time data synchronization.

Main Results:

  • The ResNet-TCN model achieved a root mean square error (RMSE) of 5.46 μm for tool wear prediction on the PHM 2010 dataset.
  • The proposed framework demonstrated superior performance compared to traditional LSTM models.
  • Effective elimination of information silos was achieved, enhancing data integration.

Conclusions:

  • The digital twin-driven PHM framework offers a responsive, scalable, and accurate solution for smart manufacturing.
  • The ResNet-TCN model shows significant potential for precise tool wear prediction.
  • Real-time data synchronization via the cyber-physical platform is crucial for effective PHM.