Jinsu Gim

This paper presents a novel method to eliminate halo gloss transition surface defects commonly found on large-scale injection-molded parts. Due to the increasing importance of product appearance and its influence on consumer decisions, the halo defects that deteriorate surface appearance and quality should be eliminated. The effect of the main injection molding process parameters on the halo defects was compared and investigated by correlating the injection molding simulations and defect specimens. It was found that a drastic increase in flow front speed (FFS) is the main cause of the halo defects. A multi-step injection speed approach that minimizes a drastic increase of FFS due to the reduction of flow front area according to the filling patterns is proposed. The proposed approach was experimentally validated for a simple test part and a real-world, commercial home appliance part, respectively. This approach can contribute to reducing the time for setting defect-free injection molding conditions before mass production.

The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes.

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.

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.

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.

The effect of an in‐mold static mixer on the optical properties of polystyrene (PS) parts was explored within the injection molding process. Several mixers were assessed via simulation and molding trials to identify the mixing ability and effect on optical properties including retardation and birefringence. It was found that the static mixers within the runner could successfully mix the polymer and disrupt property distributions including temperature but that there was only a slight improvement in retardation with some of the mixer cases. The experiments and simulations showed relatively good correlation in results although there were slight differences in the trends that could be due to the experimental measurement resolution or unaccounted‐for variables between the experiments and simulations. The retardation was experimentally measured using a custom‐made polariscope using photography and image processing. These experiments indicated that the use of a static mixer within the runner system of a mold could be used for homogenizing the polymer melt after the plasticizing unit. However, its effect on improving the optical performance of injection molded parts could be offset by the melt flow downstream of the static mixer and the potential increase of residual stresses due to flow restriction, suggesting the importance of mixer location and geometry.