3D Printing Rapid Prototyping Research Articles

A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers.

In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

3D-printing rapid prototyping used in classification of acetabular fractures and education of young surgeons

Objective To explore the application of 3D-printing rapid prototyping in classification of acetabular fractures and education of young surgeons. Methods The data of 20 patients with acetabular fracture were reviewed in this study who had been treated between January and June 2016. Three junior orthopedic surgeons and 3 senior ones were chosen as observers. The conventional radiographs (X-ray films of the pelvis and acetabulum, CT scans and 3D reconstruction images of the pelvis) and 3D-printing rapid prototyping models of the 20 patients(1∶1) were randomly numbered. All the observers were asked to make Letournel-Judet classification of each radiograph and 3D-printing model independently in the first assessment. Four weeks later in the second assessment, all the observers were asked to make the same classifications after all the conventional radiographs and 3D-printing models were randomly numbered again. The kappa statistics was used to evaluate inter- and intra-observer agreements among the recorded results. Results At the first assessment, the inter-observer agreement was 0.887 and 0.962 respectively for conventional radiographs and 3D-printing models in senior surgeons while 0.659 and 0.849 in junior surgeons. The second assessment showed the intra-observer agreement was 0.906 for radiographs and 0.925 for 3D-printing models in senior surgeons while 0.696 and 0.849 in the junior ones. Conclusions Compared with conventional radio-graphs, since 3D-printing models can effectively enhance the reliability of acetabular fracture classification, they may be more helpful for young surgeons in understanding acetabular fractures. Key words: Acetabulum; Fractures, bone; Classification; Medical education; 3D-printing technology