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Zhao, J., Yang, Z., Chen, Q., Zhang, C., Zhao, J., Zhang, G., et al. (2025) Real-Time Detection of Powder Bed Defects in Laser Powder Bed Fusion Using Deep Learning on 3D Point Clouds. Virtual and Physical Prototyping, 20, e2449171.
https://doi.org/10.1080/17452759.2024.2449171

has been cited by the following article:

• TITLE: Research on Biomimetic Additive Manufacturing Technology Based on Digital Twin

  AUTHORS: Yongqi Chen, Yuduo Wei, Shaopeng Zheng

  KEYWORDS: Additive Manufacturing, Biomimetic Additive Manufacturing, Physical Model, Data-Driven, Digital Twin

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.12, December 23, 2025

  ABSTRACT: Biomimetic additive manufacturing (Biomimetic 3D printing) is an emerging technology based on the principle of biological growth and forming to design the biological-like complex structure and biomimetic function of three-dimensional solid structure, and the use of 3D printing technology to realize the processing and manufacturing of components. Digital twin enables the full-cycle optimization and enhancement of biomimetic additive manufacturing by establishing an accurate and dynamic linkage between the physical process and its virtual counterpart. It analyzes the characteristics and problems of existing additive manufacturing technology; Combined with the existing research progress, based on the physical model and data driven perspective, the development status and existing problems of digital twin theory in additive manufacturing process are introduced. The concept and development status of biomimetic additive manufacturing were discussed. The technical framework and challenges of biomimetic additive manufacturing technology driven by digital twin system are put forward to provide reference for the development and application of biomimetic additive manufacturing technology.