3D Printed Biomimetic Metamaterials with Graded Porosity and Tapering Topology for Improved Cell Seeding and Bone Regeneration.

Biomimetic metallic biomaterials prepared for bone scaffolds have drawn more and more attention in recent years. However, the topological design of scaffolds is critical to cater to multi-physical requirements for efficient cell seeding and bone regeneration, yet remains a big scientific challenge owing to the coupling of mechanical and mass-transport properties in conventional scaffolds that lead to poor control towards favorable modulus and permeability combinations. Herein, inspired by the microstructure of natural sea urchin spines, biomimetic scaffolds constructed by pentamode metamaterials (PMs) with hierarchical structural tunability were additively manufactured via selective laser melting. The mechanical and mass-transport properties of scaffolds could be simultaneously tuned by the graded porosity (B/T ratio) and the tapering level (D/d ratio). Compared with traditional metallic biomaterials, our biomimetic PM scaffolds possess graded pore distribution, suitable strength, and significant improvements to cell seeding efficiency, permeability, and impact-tolerant capacity, and they also promote in vivo osteogenesis, indicating promising application for cell proliferation and bone regeneration using a structural innovation.

A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms.

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets.

Uncertainty quantification and validation of 3D lattice scaffolds for computer-aided biomedical applications.

A methodology is proposed for uncertainty quantification and validation to accurately predict the mechanical response of lattice structures used in the design of scaffolds. Effective structural properties of the scaffolds are characterized using a developed multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process and minimize the experimental cost, high-resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling method facilitates the process of determining homogenized strut properties to reduce the computational cost of the detailed simulation model for the scaffold. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also developed to assess the predictive capability of the stochastic upscaling method used at the strut level and lattice structure level. In comparison with physical compression test results, the proposed methodology of linking the uncertainty quantification with the multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure with minimal experimental cost by accounting for the uncertainties induced by the additive manufacturing process.