Design and Synthesis of P(AAm-co-NaAMPS)-Alginate-Xanthan Hydrogels and the Study of Their Mechanical and Rheological Properties in Artificial Vascular Graft Applications.

Cardiovascular diseases (CVDs) are the number one cause of mortality among non-communicable diseases worldwide. Expanded polytetrafluoroethylene (ePTFE) is a widely used material for making artificial vascular grafts to treat CVDs; however, its application in small-diameter vascular grafts is limited by the issues of thrombosis formation and intimal hyperplasia. This paper presents a novel approach that integrates a hydrogel layer on the lumen of ePTFE vascular grafts through mechanical interlocking to efficiently facilitate endothelialization and alleviate thrombosis and restenosis problems. This study investigated how various gel synthesis variables, including N,N'-Methylenebisacrylamide (MBAA), sodium alginate, and calcium sulfate (CaSO4), influence the mechanical and rheological properties of P(AAm-co-NaAMPS)-alginate-xanthan hydrogels intended for vascular graft applications. The findings obtained can provide valuable guidance for crafting hydrogels suitable for artificial vascular graft fabrication. The increased sodium alginate content leads to increased equilibrium swelling ratios, greater viscosity in hydrogel precursor solutions, and reduced transparency. Adding more CaSO4 decreases the swelling ratio of a hydrogel system, which offsets the increased swelling ratio caused by alginate. Increased MBAA in the hydrogel system enhances both the shear modulus and Young's modulus while reducing the transparency of the hydrogel system and the pore size of freeze-dried samples. Overall, Hydrogel (6A12M) with 2.58 mg/mL CaSO4 was the optimal candidate for ePTFE-hydrogel vascular graft applications due to its smallest pore size, highest shear storage modulus and Young's modulus, smallest swelling ratio, and a desirable precursor solution viscosity that facilitates fabrication.

A Novel Design Method of an Evolutionary Mold Cooling Channel Using Biomimetic Engineering.

In this study, an evolutionary cooling channel, a new methodology for designing a conformal cooling channel, was proposed. This methodology was devised by imitating the way that a plant's roots grow towards a nutrient-rich location. Additionally, Murray's law was applied to increase the cooling efficiency through minimizing the pressure loss of the cooling water inside the cooling channel. The proposed method was applied to the specimen shape to verify the concept, and it was confirmed that efficient cooling was achieved by applying it to the headlamp lens cover part of an actual vehicle. When this methodology was applied, the temperature deviation of the part could be improved by about 46% in just third generations, and the pressure loss could be reduced by about 10 times or more compared to the result of applying the straight-line cooling channel.

Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding.

Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis.

Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model.

The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.

Causes of the Gloss Transition Defect on High-Gloss Injection-Molded Surfaces.

The gloss transition defect of injection-molded surfaces should be mitigated because it creates a poor impression of product quality. Conventional approaches for the suppression of the gloss transition defect employ a trial-and-error approach and additional equipment. The causes of the generation of a low-gloss polymer surface and the surface change during the molding process have not been systematically analyzed. This article proposes the causes of the generation of a low-gloss polymer surface and the occurrence of gloss transition according to the molding condition. The changes in the polymer surface and gloss were analyzed using gloss and topography measurements. The shrinkage of the polymer surface generates a rough topography and low glossiness. Replication to the smooth mold surface compensates for the effect of surface shrinkage and increases the surface gloss. The surface stiffness and melt pressure influence the degree of mold surface replication. The flow front speed and mold temperature are the main factors influencing the surface gloss because they affect the development rate of the melt pressure and the recovery rate of the surface stiffness. Therefore, the mold design and process condition should be optimized to enhance the uniformity of the flow front speed and mold temperature.