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  4. Vol 3, Issue 1, 2023

Digital Twin Modeling and Simulation of Computer Aided Design and Manufacturing Structure: Case Study

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DOI: 10.23977/dmpm.2023.030101 | Downloads: 19 | Views: 596

Author(s)

Safiye Turgay 1, Necip Akar 1

Affiliation(s)

1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Safiye Turgay

ABSTRACT

This paper explores the application of digital twin technology in the CAD/CAM domain, focusing on its potential to enhance the design and manufacturing structure. The paper begins by introducing the concept of digital twins and their role in bridging the gap between physical and virtual worlds. It emphasizes the benefits of real-time data synchronization and it enables continuous monitoring, analysis, and decision-making throughout the product lifecycle in detail. Next, the focus shifts to the integration of digital twin modelling into the CAD/CAM processes. The paper outlines the steps involved in creating a digital twin of the design and manufacturing structure, from data acquisition and integration to model calibration and validation. Special attention is given to ensuring the accuracy and fidelity of the digital twin to enable reliable simulation results. The paper then explores the various simulation capabilities offered by the digital twin model. It delves into the use of finite element analysis (FEA), computational fluid dynamics (CFD), and other simulation techniques to analyse product performance, optimize manufacturing processes, and assess structural integrity. Case studies demonstrate the application of digital twin simulations in improving design efficiency, reducing time-to-market, and enhancing overall product quality.

KEYWORDS

Digital Twin, Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), Design and Manufacturing Structure, Continuous Monitoring

CITE THIS PAPER

Safiye Turgay, Necip Akar, Digital Twin Modeling and Simulation of Computer Aided Design and Manufacturing Structure: Case Study. Digital Manufacturing and Process Management (2023) Vol. 3: 1-10. DOI: http://dx.doi.org/10.23977/dmpm.2023.030101.

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