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The main limitations of currently available artificial spinal discs are geometric unfit and unnatural motion. Multi-material additive manufacturing (AM) offers a potential solution for the fabrication of personalized free-form implants with a better fit and variable material distribution to achieve a set of target physiological stiffnesses. The structure of the artificial spinal disc proposed in this paper is inspired from a natural disc and includes both a matrix and a crisscross fiber-like structure, where the design variables are their material properties. After carrying out design variable reduction using linking strategies, a finite element-based optimization is then conducted to calculate the optimized material distribution to achieve physiological stiffness under five loading cases. The results show a good match in stiffness of the multi-material disc compared with the natural disc and that the multi-material artificial disc outperforms a current known solution, the ball-and-socket disc. Moreover, the potential of achieving an improved match in stiffness with a larger range of available 3D printable materials is demonstrated. Although the direct surgical implantation of the design is hindered currently by the biocompatibility of the 3D printed materials, a potential improvement of the design proposed is shown.

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A personalized, 3D printed, multi-material artificial spinal disc is expected to not only achieve personalized anatomical fit, but also to restore the natural mechanics of the implanted spinal segment. However, the necessary structure for disc design is not explored and optimizing the design is challenging due to the high-dimensional search space provided by the material distribution precision of multi-material 3D printing as well as necessary nonlinear finite element simulation. Therefore, this study explores the feasibility of two multi-material spinal disc designs and a clustering-based design variable linking method to achieve efficient and effective optimization. The optimization goal is to enable the implant to have natural stiffnesses for five loading cases. The results show that a biomimetic fiber network is necessary for the disc design. Moreover, the optimization performance of the heuristic derived from a clustering-based method is shown to be a good trade-off between the objective function value and the computational time.

Due to the limitations of currently available artificial spinal discs stemming from anatomical unfit and unnatural motion, patient-specific elastomeric artificial spinal discs are conceived as a promising solution to improve clinical results. Multimaterial Additive Manufacturing (AM) has the potential to facilitate the production of an elastomeric composite artificial disc with complex personalized geometry and controlled material distribution. Motivated by the potential combined advantages of personalized artificial spinal discs and multi-material AM, a biomimetic multi-material elastomeric artificial disc design with several matrix sections and a crisscross fiber network is proposed in this study. To determine the optimized material distribution of each component for natural motion restoration, a computational method is proposed. The method consists of automatic generation of a patient-specific disc Finite Element (FE) model followed by material property optimization. Biologically inspired heuristics are incorporated into the optimization process to reduce the number of design variables in order to facilitate convergence. The general applicability of the method is verified by designing both lumbar and cervical artificial discs with varying geometries, natural rotational motion ranges, and rotational stiffness requirements. The results show that the proposed method is capable of producing a patient-specific artificial spinal disc design with customized geometry and optimized material distribution to achieve natural spinal rotational motions. Future work focuses on extending the method to also include implant strength and shock absorption behavior into the optimization as well as identifying a suitable AM process for manufacturing.