Mechanical characterization of three-dimensional (3D) printed meta-biomaterials is rapidly becoming a crucial step in the development of novel medical device concepts, including those used in functionally graded implants for orthopedic applications. Finite element simulations are a valid, FDA-acknowledged alternative to experimental tests, which are time-consuming, expensive, and labor-intensive. However, when applied to 3D-printed meta-biomaterials, state-of-the-art finite element modeling approaches are becoming increasingly complex, while their accuracy remains limited. A critical condition for accurate simulation results is the identification of correct modelling parameters. This study proposes a machine learning-based strategy for identifying model parameters, including material properties and model boundary conditions, to enable accurate simulations of macro-scale mechanical behavior. To achieve this goal, a physics-informed artificial neural network model (PIANN) was developed and trained using data generated through a fully automated finite element modeling workflow. Subsequently, the PIANN model was then tested using real experimental force-displacement data as its input. The experimental data from 3D-printed structures were used to predict the associated parameters for finite element modeling. Finally, the workflow was validated by qualitatively and quantitatively comparing simulation results to the experimental data. Based on these results, we concluded that the proposed workflow could identify model parameters such that the predictions of associated finite element simulations are in agreement with experimental observations. Furthermore, resulting finite element models were found to outperform state-of-the-art models in terms of both quantitative and qualitative accuracy. Therefore, the proposed strategy has the potential to facilitate the broader application of finite element simulations in evaluating 3D-printed parts, in general, and 3D-printed meta-biomaterials, in particular.

Classification and evaluation of acetabular defects remain challenging and are primarily based on qualitative classification methods. That is because quantitative techniques describing variations of acetabular defects and accompanying bone loss volume are not available. This study introduces a new method based on statistical shape models (SSMs) to quantitively describe acetabular defects. This method is then applied to 87 acetabular defects to objectively describe the variations in acetabular defects typically encountered during revision total hip arthroplasty. The absolute bone loss volume, relative bone loss volume, and relative bone loss surface area with respect to the SSM-based pre-diseased anatomy were used to quantify the acetabular bone defects in different segments of the acetabular surface. The absolute bone loss volume of the average defect shape was equal to 37.0 cm³. The first three principal modes, accounting for 62% of the total shape variation, were found to represent variations in acetabular defect morphology. The first, second, and third principal modes described, respectively, the size of the bone defects, the difference between superomedially and superolaterally migrated defects, and the degree of involvement of the posterior or anterior column. The developed SSM and the introduced approach could be used to create automated and unbiased classification methods based on quantitative data. Moreover, the proposed model and the underlying data provide the basis for a quantitative design approach where the shape and size of new acetabular implants are determined according to clinical variation present in acetabular defects.