Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm

Aug 4, 2026

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Inverse design of anisotropic bone scaffolds is addressed by reversing the conventional workflow that maps microstructure to mechanical properties. A machine-learning-driven model couples a backpropagation neural network with a regenerative genetic algorithm to match bone anisotropy by varying TPMS cell counts along different directions. Finite element analysis generates TPMS configurations and training data, while the learned microstructure–elasticity relationship guides inverse optimization. Validation compares the elasticity matrix of inverse-designed structures to actual bone, achieving average errors under 3% for three targets and ~5% for six.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Background and motivation

Methods

Machine learning model and finite element data

Regenerative genetic algorithm for inverse design

Results

Accuracy against target elasticity matrix

Discussion

Design efficiency and controllability

TYPE Original Research PUBLISHED 07 September 2023 DOI 10.3389/fbioe.2023.1241151 OPEN ACCESS EDITED BY Andrew T. M. Phillips, Imperial College London, United Kingdom REVIEWED BY Qiang Chen, Southeast University, China Linjie Wang, Imperial College London, United Kingdom *CORRESPONDENCE Yadong Liu, [doctoryadong@163.com](doctoryadong@163.com) RECEIVED 16 June 2023 ACCEPTED 25 August 2023 PUBLISHED 07 September 2023 CITATION Liu W, Zhang Y, Lyu Y, Bosiakov S and Liu Y (2023), Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm. Front. Bioeng. Biotechnol. 11:1241151 . doi: 10.3389/fbioe.2023.1241151 COPYRIGHT © 2023 Liu, Zhang, Lyu, Bosiakov and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm Wenhang Liu 1, Youwei Zhang 1, Yongtao Lyu 1,2, Sergei Bosiakov 3 and Yadong Liu 4* 1Department of Engineering Mechanics, Dalian University of Technology, Dalian, China, 2DUT-BSU Joint Institute, Dalian University of Technology, Dalian, China, 3Faculty of Mechanics and Mathematics, Belarusian State University, Minsk, Belarus, 4Department of Orthopedics, Dalian Municipal Central Hospital Afﬁliated of Dalian University of Technology, Dalian, China Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial. Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The ﬁnite element (FE) method was used to calculate the TPMS conﬁguration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design. Results: The accuracy of the BPNN-RGA model was conﬁrmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets. Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efﬁciency, compared to traditional optimization methods. The entire design process is easily controlled. KEYWORDS machine learning, genetic algorithm, triply periodic minimal surfaces, inverse design, arrangement anisotropy 1 Introduction Bone is a crucial part of the human body, serving various functions such as body support, protection of internal organs, and mineral storage. With the increasing aging population, the number of people suffering from joint diseases is also rising, leading to a greater demand for external repair techniques for bone defects (Gruskin et al., 2012; Li et al., 2015; Lin et al., 2020). Currently, the most important treatment method for re

FAQs

Why is inverse design important for anisotropic bone scaffolds?

Current TPMS scaffold design largely follows a forward process from microstructure to mechanics. Inverse design enables scaffolds to be created directly from target mechanical properties, which is crucial because bone properties vary across directions.

How does the proposed method represent anisotropy in the TPMS scaffold?

Anisotropy is matched by changing the number of cells in different directions within the TPMS structure.

How accurate is the inverse-designed scaffold compared with actual bone?

Model accuracy is confirmed by comparing elasticity matrices. The average error is below 3.00% for three mechanical performance targets and about 5.00% for six targets.

Aug 4, 2026

Upload by Patrick