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Related Experiment Video

Updated: Sep 26, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Published on: August 5, 2021

Refined Simulation Method for Computer-Aided Process Planning Based on Digital Twin Technology.

Yupeng Xin¹, Yiwen Chen¹, Wenhui Li²

¹College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

|April 23, 2022

Summary

This summary is machine-generated.

Digital twin technology enhances simulation accuracy in computer-aided process planning (CAPP). This approach integrates real-time data with process models, improving machining simulations by 58% compared to traditional methods.

Keywords:

computer-aided process planning (CAPP)digital twin machining manufacturing systems engineering simulation

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Area of Science:

Manufacturing Engineering
Digital Twin Technology
Process Simulation

Background:

Computer-aided process planning (CAPP) relies on simulation for error prediction and optimization.
Simulation accuracy is critical for effective process decision-making and optimization.
Traditional simulation methods lack dynamic association with real-time manufacturing data.

Purpose of the Study:

To develop a high-fidelity process model using digital twin technology for enhanced CAPP.
To achieve dynamic association between real-time manufacturing data and process models.
To improve the accuracy of machining process simulations.

Main Methods:

Digital twin technology integrated with CAPP/MES systems.
Wavelet transform for noise reduction of surface inspection data.
Poisson reconstruction algorithm for high-fidelity process model creation.
Matlab for surface topography reconstruction.

Main Results:

Successful integration of real-time surface inspection data with the digital twin process model.
Effective noise reduction of high-frequency detection signals, verified by SNR calculations.
High-fidelity process model reconstruction enabling refined subsequent process simulations.
A 58% improvement in simulation accuracy compared to traditional methods.

Conclusions:

Digital twin technology significantly enhances the accuracy of machining process simulations within CAPP.
Dynamic data association is key to building high-fidelity process models.
The proposed method offers a substantial improvement over traditional simulation approaches in manufacturing.