Advanced Manufacturing of Biomedical Scaffolds: Modeling Simulation and Process Optimization Approaches
Main Article Content
Keywords
Biomedical scaffolds, Tissue engineering, Computational modeling, Finite element modeling (FEM), Computational fluid dynamics (CFD), Additive manufacturing, Regenerative medicine, Bone tissue engineering (BTE), 3D polymeric scaffolds
Abstract
Background: It can be vital in the process of tissue engineering and regenerative medicine since biomedical scaffolds offer the structural support that allows the process to enable cell attachment, cell proliferation, transporting nutrients, and metabolic waste. The traditional scaffold fabrication techniques like the solvent casting and freezedrying are usually less able to provide a control over the scaffold architecture and mechanical characteristics. The current developments in additive manufacturing (AM) and computational modeling have provided a new opportunity in generating highly regulated and functional scaffolds in bone and orthopedic tissue engineering. Objective: The proposed review will address the latest works in the high-level production of biomedical scaffolds with the combination of additive manufacturing methods with computational modeling and data-driven optimization strategies to improve the design and functioning of scaffolds. Materials and Methods: Different additive manufacturing methods, such as stereolithography, selective laser sintering/selective laser melting, fused deposition modeling, electron beam melting, laser-engineered net shaping, two-photon polymerization, and laser-based bioprinting among others are discussed. Such manufacturing methods are complemented by computational algorithms like finite element modeling (FEM), computational fluid dynamics (CFD), and machine learning (ML) to control the geometry of scaffolds, mechanical behavior, and mass transport. Biodegradable polymers, collagen, and ceramic-based composites as scaffold materials are also tested on the basis of mechanical strength, bioactivity, and the degrading nature. Results: Additive manufacturing can provide control of pore architecture, pore size, and interconnectivity, both in control with customized scaffolds made with increased biological functionality. Computational modeling like the FEM and CFD can be used to predict the mechanical rigidity, stress distribution, and fluid transport in scaffolds. The best scaffold performance can be noticed by ensuring that the values of the elastic modulus remain between 0.1–10 Gpa, pore interconnectivity is more than 90 percent, and shear stress degree lies between 0.1–1.0 Pascals to support cell growth and transport of nutrients. The machine learning methods also speed up the design of the scaffolds by minimizing the number of experiments and also predicting how to optimize scaffolding designs. Conclusion: The combination of additive manufacturing and FEM, CFD, and machine learning will be a potent platform to develop the next generation biomedical scaffolds with enhanced mechanical stability and biological performance. Such computationally controlled production methods allow the accurate stress distribution, permeability and nutrient transport to be predicted resulting in a more efficient development of scaffolds. Although there are still some barriers surrounding manufacturing variability, in vivo validation and regulatory issues, these methods promise a great deal in terms of creating personalized regenerative medicine and orthopedic tissue engineering.
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Apr 27, 2026
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Vasudev, H., Mehta, A., & Prasad, B. (2026). Advanced Manufacturing of Biomedical Scaffolds: Modeling Simulation and Process Optimization Approaches. Global Clinical Engineering Journal, 8(2), 34–55. https://doi.org/10.31354/globalce.v8i2.374
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