Recently, studies on the development of flexible electronic devices by combining flexible materials and a conductor have been actively performed as interest in wearable devices. Especially, carbon nanotubes (CNT) or graphene coating have been used to construct a circuit to induce improvement in flexibility and rigidity. Various technologies have been developed in the surface coating of conductive materials, which are key to the manufacture of flexible electronic devices. Surface coating products with 3D coating and micro-patterns have been proposed through electrospinning, electrification, and 3D printing technologies. As a result of this advanced surface coating technology, there is a growing interest in manufacturing gradient conductive surfaces. Gradient surfaces have the advantage that they are adapted to apply a gentle change or to inspect optimum conditions in a particular region by imparting continuously changing properties. In this study, we propose a manufacturing technique to produce a continuous gradient conductive surface by combining a partial stretching of elastomer and a conductive material coating, and introduce experimental results to confirm its performance.

In this study, a study was carried out that could effectively produce a heat dissipation effect on plastic materials. Using carbon nanotube (CNT), aluminum powder and plastic, the material properties were tested in 2 cases of compounding ratio. The test sample mold was designed and constructed prior to the experiment. The experiments include tensile strength, elongation rate, flexural strength, flexural elasticity rate, eye-jaw impact strength, gravity and thermal conductivity. Results from 60% and 70% mixture of aluminium to plastic were tested, and a 10% less combined result was a relatively good property. For research purposes, the heat dissipation effect and light weighting obtained a good measure when the combined amount of Al was 60%.

Fluid jet polishing is a method of jetting a fluid to polish a concave or free-form surface. However, the fluid jet method is difficult to form a stable polishing spot because of the lack of concentration. In order to solve this problem, MR fluid jet polishing system using an abrasive mixed with an MR fluid whose viscosity changes according to the intensity of a magnetic field is under study. MR fluid jet polishing is not easy to formulate for precise optimal conditions and material removal due to numerous fluid compositions and process conditions. Therefore, in this paper, quantitative data on the factors that have significant influence on the machining conditions are presented using various simulations and the correlation studies are conducted. In order to verify applicability of the fabricated MR fluid jet polishing system by nozzle diameter, the flow pattern and velocity distribution of MR fluid and polishing slurry of MR fluid jet polishing were analyzed by flow analysis and shear stress due to magnetic field changes was analyzed. The MR fluid of the MR fluid jet polishing and the flow pattern and velocity distribution of the polishing slurry were analyzed according to the nozzle diameter and the effects of nozzle diameter on the polishing effect were discussed. The analysis showed that the maximum shear stress was 0.45 mm at the diameter of 0.5 mm, 0.73 mm at 1.0 mm, and 1.24 mm at 1.5 mm. The cross-sectional shape is symmetrical and smooth W-shape is generated, which is consistent with typical fluid spray polishing result. Therefore, it was confirmed that the high-quality surface polishing process can be stably performed using the developed system.

In the mold industry, CAM software has been introduced to solve the impossible or time-consuming part of the mold industry because the increase in labor costs, the drop in mold price, and the short delivery time are tasks to be solved not only in the manufacturing industry but also in the mold industry as a whole. In order to reduce the processing time and improve the surface roughness, we have been conducting various researches for efficient machining. This study was carried out to compare the ball end mill and radius end mill tools with the Power mill software and NC brain software under the same conditions and to find out the most efficient method of machining the smooth drafting surface and improving the surface roughness. (1) By machining the smooth drafting surface using radius end mill, the machining time is 23.7% faster than when using ball end mill. (2) Surface roughness when machined with radius end mill is smoother than when using ball end mill. Based on these results, it can not be applied to all shapes, but if it is a relatively wide and simple gradient shape, the raster machining method using radius end mill can be more effective in terms of delivery and quality than machining with ball end mill.

In this study, a study was carried out that could effectively produce a heat dissipation effect on plastic materials. Using carbon nanotube (CNT), aluminum powder and plastic, the material properties were tested in 2 cases of compounding ratio. The test sample mold was designed and constructed prior to the experiment. The experiments include tensile strength, elongation rate, flexural strength, flexural elasticity rate, eye-jaw impact strength, gravity and thermal conductivity. Results from 60% and 70% mixture of aluminium to plastic were tested, and a 10% less combined result was a relatively good property. For research purposes, the heat dissipation effect and light weighting obtained a good measure when the combined amount of Al was 60%.

Cell phone injection is characterized by its small size and thinness. In addition, the product has a short cycle time, requiring a very short production schedule. To produce more accurate products faster, data from experience in producing similar products is required. In this study, two mobile phone models are presented. In this study, the quality problems caused by molding analysis and actual injection molding were analyzed and made into a database. As a result, it was considered that all the defects in the molding analysis do not affect the product in some cases, rather than appear as defects in the actual product. All defects shown in the molding analysis can be made into a database, and based on this data, it will be possible to obtain an effect that can predict more accurately whether it will cause problems after injection.

As the consumer market in the cosmetic, vehicle manufacturing and aerospace industries grows, the demand for manufacturing industries using on injection mold technology. Also, such manufacturing technology of metal machining is expensive, and the shape is limited. Cosmetic cushion fact products are divided into outer relevant to the exterior of the product and inner containers containing the actual contents. In the case of the inner container, it needs to be combined with the upper and lower cases. As environmental regulations are strengthened internationally, the use of a large number of component parts can result in significant losses in recycling and economics. Therefore, this study aims to perform injection molding analysis through injection molding simulation to develop a cushion fact container that can be recycled through the unification of products and materials using polypropylene to cope with environmental regulations. In the case of injection molding conditions, Injection Time(sec): 4.5, Cooling Time(sec): 13, Resin Temperature($^{\circ}C$): 240, and Pressure(MPa): 30 were determined. The results of injection molding simulation according to the two design methods were compared with the sync mark which shows the problem of filling and injection molding.

In conventional machining, the use of cutting fluid is essential to reduce cutting heat and to improve machining quality. However, to increase the performance of cutting fluids, various chemical components have been added. However, these chemical components during machining have a negative impact on the health of workers and cutting environment. In current machining, environment-friendly machining is conducted using MQL (minimum quantity lubrication) or cryogenic air spraying to minimize the harmful effects. In this study, the injection nozzle that can combined injecting minimum quantity lubrication(MQL) and cryogenic gas was designed and the shape optimization was performed by using computational fluid dynamics(CFD) and design of experiment(DOE). Performance verification was performed for the designed nozzle. The diameter of the sprayed fluid at a distance of 30 mm from the nozzle was analyzed to be 21 mm. It was also analyzed to lower the aerosol temperature to about 260~270K.

As the consumer market in the mold, automation and aerospace industries grows, the demand for chemical machining using on electrochemical polishing increases. To enhance the surface roughness and gloss of the micro-needle, we have studied for an electrochemical polishing. Electrochemical polishing requires the chemical reaction of solution and material according to the electrolyte and electrode. In this study, sulfuric acid(30%), phosphoric acid(50%), and DI-water(20%)were used as the electrolytic solution, and the electrolytic solution temperature used $58^{\circ}C$. Electrochemical polishing was carried out in experimental conditions, and the micro-needle experiment was carried out from the basic experiment to obtain the experimental conditions. Experimental results show that as the voltage and current increase, the surface roughness improved and the gloss is improved. So, the best result for this experiment was obtained in condition 6, which improved micro-needle.

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.