Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects

Cellular materials have a wide range of applications, including structural optimization and biomedical applications. Due to their porous topology, which promotes cell adhesion and proliferation, cellular materials are particularly suited for tissue engineering and the development of new structural s...

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Bibliographic Details
Published in:Materials 2023-05, Vol.16 (11), p.3946
Main Authors: Pais, A I, Belinha, J, Alves, J L
Format: Article
Language:English
Subjects:
Additive manufacturing
Algorithms
Artificial intelligence
Biological products
Biomechanical engineering
Biomechanics
Biomedical materials
Biomimetics
Bones
Cell adhesion
Cellular structure
Corrosion resistance
Deep learning
Functionally gradient materials
Geometry
Laser sintering
Machine learning
Mechanical analysis
Mechanical properties
Metals
Methods
Optimization
Orthopedics
Permeability
Pore size
Porosity
Review
Scaffolds
Stainless steel
State-of-the-art reviews
Stiffness
Stress shielding
Tissue engineering
Titanium alloys
Topology
Transplants & implants
Trends
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Summary:Cellular materials have a wide range of applications, including structural optimization and biomedical applications. Due to their porous topology, which promotes cell adhesion and proliferation, cellular materials are particularly suited for tissue engineering and the development of new structural solutions for biomechanical applications. Furthermore, cellular materials can be effective in adjusting mechanical properties, which is especially important in the design of implants where low stiffness and high strength are required to avoid stress shielding and promote bone growth. The mechanical response of such scaffolds can be improved further by employing functional gradients of the scaffold's porosity and other approaches, including traditional structural optimization frameworks; modified algorithms; bio-inspired phenomena; and artificial intelligence via machine learning (or deep learning). Multiscale tools are also useful in the topological design of said materials. This paper provides a state-of-the-art review of the aforementioned techniques, aiming to identify current and future trends in orthopedic biomechanics research, specifically implant and scaffold design.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma16113946