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Computational optimization of 3D printed bone scaffolds using orthogonal array-driven FEA and neural network

Amulya Shetty¹, Aamirah Fathima¹, B Anika¹

¹Father Muller Medical College, Mangalore, Karnataka, 575002, India.

Scientific Reports

|August 20, 2025

Summary

This study optimizes 3D printed polylactic acid (PLA) scaffolds for bone tissue engineering. The Gyroid lattice geometry with 2.0 mm thickness offers superior mechanical performance, validated by AI and simulations.

Keywords:

Additively manufactured scaffolds Artificial neural network Bone tissue engineering Finite element analysis Mechanical characterization Microstructural analysis Soft computing techniques Taguchi design of experiments

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Area of Science:

Biomaterials Science
Orthopedic Engineering
Tissue Engineering

Background:

Bone tissue engineering seeks to regenerate damaged bone using biomimetic scaffolds and advanced materials.
Optimizing scaffold design is crucial for mechanical integrity and biological performance in bone regeneration.

Purpose of the Study:

To optimize 3D printed polylactic acid (PLA) lattice scaffolds for bone tissue engineering applications.
To investigate the influence of geometric configuration and processing parameters on scaffold mechanical performance.

Main Methods:

Development of Lidinoid, Diamond, and Gyroid lattice scaffolds with varying wall thicknesses (1.0-2.0 mm).
Application of Taguchi L27 Orthogonal Array for experimental design and mechanical testing under compressive loads (3-9 kN).
Utilized Back-propagation Artificial Neural Network (BPANN) for predictive modeling and Finite Element Analysis (FEA) for validation.

Main Results:

The Gyroid lattice with 2.0 mm thickness demonstrated superior mechanical integrity, exhibiting minimal displacement (0.36 mm) and strain (1.2 x 10⁻²) at 3 kN.
Lidinoid structures showed the highest deformability under tested conditions.
BPANN model achieved high prediction accuracy (R² > 0.995) for scaffold displacement and strain.

Conclusions:

The Gyroid lattice geometry represents a promising scaffold design for bone tissue engineering due to its enhanced mechanical properties.
An integrated approach combining experimental design, machine learning, and FEA provides a robust framework for optimizing scaffold architecture.
Optimized scaffolds have significant implications for improving mechanical strength and biological outcomes in bone healing applications.