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<p>Porous fusion cages, as critical medical implants, face significant design challenges due to their intricate internal lattice structure, which must be precisely matched to biomechanical requirements to facilitate bone integration. Traditional computer-aided design (CAD), using manual tuning and iterative finite element analysis (FEA), struggles with optimization efficiency. To address this, we propose a machine learning-based Inverse–Forward Cooperative Design Network (IFCDN), an intelligent CAD framework to automate the performance-driven design of porous structures. We constructed parametric models for strut-based (SC, FCC, BCC) and TPMS (Gyroid, Diamond) lattices and utilized FEA to generate a dataset correlating geometric features with biomechanical properties (elastic modulus, yield strength, and porosity). Furthermore, to address the inherent one-to-many mapping challenge in inverse design, the IFCDN employs a Classifier-Guided Conditional Variational Generative Adversarial Network (CG-CVAE-GAN) to overcome the mode averaging effect of traditional regression models, enabling effective inverse generation from target biomechanical properties to lattice geometric parameters and types. Additionally, an XGBoost-based forward prediction model is integrated to form an “inverse design-forward prediction” closed-loop optimization. Experimental results for four clinical scenarios demonstrate that the IFCDN automatically selects optimal topologies, with average relative errors between the predicted performance of the optimized designs and the FEA validation results being 0.92%, 2.04%, 1.82%, and 0.95%, respectively. This framework significantly improves efficiency while ensuring design accuracy, offering an intelligent solution for personalized fusion cage design under multiple performance constraints.</p>

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Inverse–forward design and multi-objective optimization of porous lattice structures for spinal fusion cages

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Wei Xu,
  • Kunjin He,
  • Cong Zhang,
  • Yiping Weng,
  • Zhengming Chen

摘要

Porous fusion cages, as critical medical implants, face significant design challenges due to their intricate internal lattice structure, which must be precisely matched to biomechanical requirements to facilitate bone integration. Traditional computer-aided design (CAD), using manual tuning and iterative finite element analysis (FEA), struggles with optimization efficiency. To address this, we propose a machine learning-based Inverse–Forward Cooperative Design Network (IFCDN), an intelligent CAD framework to automate the performance-driven design of porous structures. We constructed parametric models for strut-based (SC, FCC, BCC) and TPMS (Gyroid, Diamond) lattices and utilized FEA to generate a dataset correlating geometric features with biomechanical properties (elastic modulus, yield strength, and porosity). Furthermore, to address the inherent one-to-many mapping challenge in inverse design, the IFCDN employs a Classifier-Guided Conditional Variational Generative Adversarial Network (CG-CVAE-GAN) to overcome the mode averaging effect of traditional regression models, enabling effective inverse generation from target biomechanical properties to lattice geometric parameters and types. Additionally, an XGBoost-based forward prediction model is integrated to form an “inverse design-forward prediction” closed-loop optimization. Experimental results for four clinical scenarios demonstrate that the IFCDN automatically selects optimal topologies, with average relative errors between the predicted performance of the optimized designs and the FEA validation results being 0.92%, 2.04%, 1.82%, and 0.95%, respectively. This framework significantly improves efficiency while ensuring design accuracy, offering an intelligent solution for personalized fusion cage design under multiple performance constraints.