Design and Mechanical Characterization Using Digital Image Correlation of Soft Tissue-Mimicking Polymers

Post-printing processing and aging effects on Polyjet materials intended for the fabrication of advanced surgical simulators

Material Jetting (MJ) 3D printing technology is promising for the fabrication of highly realistic surgical simulators, however, the changes in the mechanical properties of MJ materials after post-printing treatments and over time remain quite unknown. In this study, we investigate the effect of different post-printing processes and aging on the mechanical properties of a white opaque and rigid MJ photopolymer, a white flexible MJ photopolymer and on a combination of them. Tensile and Shore hardness tests were conducted on homogeneous 3D-printed specimens: two different post-printing procedures for support removal (dry and water) and further surface treatment (with glycerol solution) were compared. The specimens were tested within 48 h from printing and after aging (30-180 days) in a controlled environment. All groups of specimens treated with different post-printing processes (dry, water, glycerol) exhibited a statistically significant difference in mechanical properties (i.e. elongation at break, elastic modulus, ultimate tensile strength). Particularly, the treatment with glycerol makes the flexible photopolymer more rigid, but then with aging the initial elongation of the material tends to be restored. For the rigid photopolymer, an increase in deformability was observed as a major effect of aging. The hardness tests on the printed specimens highlighted a significant overestimation of the Shore values declared by the manufacturer. The study findings are useful for guiding the material selection and post-printing processing techniques to manufacture realistic and durable models for surgical training.

Production of alginate macrocapsule device for long-term normoglycaemia in the treatment of type 1 diabetes mellitus with pancreatic cell sheet engineering

Type 1 diabetes-mellitus (T1DM) is characterized by damage of beta cells in pancreatic islets. Cell-sheet engineering, one of the newest therapeutic approaches, has also been used to create functional islet systems by creating islet/beta cell-sheets and transferring these systems to areas that require minimally invasive intervention, such as extrahepatic areas. Since islets, beta cells, and pancreas transplants are allogeneic, immune problems such as tissue rejection occur after treatment, and patients become insulin dependent again. In this study, we aimed to design the most suitable cell-sheet treatment method and macrocapsule-device that could provide long-term normoglycemia in rats. Firstly, mesenchymal stem cells (MSCs) and beta cells were co-cultured in a temperature-responsive culture dish to obtain a cell-sheet and then the cell-sheets macroencapsulated using different concentrations of alginate. The mechanical properties and pore sizes of the macrocapsule-device were characterized. The viability and activity of cell-sheets in the macrocapsule were evaluatedin vitroandin vivo. Fasting blood glucose levels, body weight, and serum insulin & C-peptide levels were evaluated after transplantation in diabetic-rats. After the transplantation, the blood glucose level at 225 mg dl-1on the 10th day dropped to 168 mg dl-1on the 15th day, and remained at the normoglycemic level for 210 days. In this study, an alginate macrocapsule-device was successfully developed to protect cell-sheets from immune attacks after transplantation. The results of our study provide the basis for future animal and human studies in which this method can be used to provide long-term cellular therapy in T1DM patients.

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.