By developing molds and facilities to horizontally mold the functional part of the dry-cast concrete block, We intend to develop molds and a series of facilities to horizontally mold the functional part of the dry-cast concrete block to increase production per cycle while maintaining existing production methods and major facilities. In order to do so, CAE analysis is first required to develop molds and facilities for horizontally molding the functional part of the drycast concrete block in the horizontal direction. The procedure will be carried out by reviewing the validity of boundary conditions and physical properties, 3D modeling, grid generation, construction of analysis models, model validity, analysis according to frequency changes, and analysis according to physical properties. First, through the comparison of two-point support, three-point support, and two-point and three-point support in the constraint conditions, We would like to compare it with the actual molded product in the horizontal direction. But first of all, it is considered two-point support in the constraint conditions in this paper.

The EV electric vehicle market is growing rapidly worldwide. Magnet fixing technology is important for mass production of driving motors, a key part of electric vehicles. The magnet fixing method was carried out by the PAM (Polyamide molding) method. This study conducted the injection of rotor core magnet PA of EV traction motor and is a study on the amount of rotor core deformation. In this study, the change in the outer diameter of the product after injection and the non-molding phenomenon were tested. An injection mold was made and the results and phenomena of product deformation types are discussed.

In this study, the change in the mold opening stroke of important functional parts according to the 20, 50, 80, and 100% increase in the injection speed of a hydraulic 150 ton hydraulic injection molding machine was studied to verify the accuracy of the injection speed and mold opening stroke and the reproducibility of the standard deviation. The null and alternative hypotheses were confirmed by conducting hypothesis verification according to the experimental condition change using the experimental design method.

High-entropy alloys (HEAs) are attracting attention because of their excellent properties and functions; however, they are relatively expensive compared with commercial alloys. Therefore, various efforts have been made to reduce the cost of raw materials. In this study, MIM is attempted using coarse equiatomic CoCrFeMnNi HEA powders. The mixing ratio (powder:binder) for HEA feedstock preparation is explored using torque rheometer. The block-shaped green parts are fabricated through a metal injection molding process using feedstock. The thermal debinding conditions are explored by thermogravimetric analysis, and solvent and thermal debinding are performed. It is densified under various sintering conditions considering the melting point of the HEA. The final product, which contains a small amount of non-FCC phase, is manufactured at a sintering temperature of 1250oC.

In this paper, the mechanical properties of glass fiber reinforced plastic (GFRP) rebar, which has been applied as an concrete reinforcement, and produced carbon fiber reinforced plastic (CFRP) grid were compared to develop a concrete reinforcement material with excellent mechanical properties. In addition, the mechanical properties of CFRP prepared with each molding process were evaluated. Three molding processes were evaluated: prepreg oven bagging, reaction injection molding (RIM), and pultrusion. The tensile strength of the CFRP grid prepared through pultrusion was 2.85GPa, the elastic modulus was 169.81GPa, and the strain was 1.68%, which was 2.85 times better in tensile strength, and 2.83 times better in elastic modulus compared mechanical properties of GFRP rebar. The strain was confirmed to be equivalent to GFRP rebar.

As the 4th industrial revolution emerges, the implementation of smart factories are essential in the manufacturing industry. However, 80% of small and medium-sized enterprises that have introduced smart factories remain at the basic level. In addition, in root industries such as injection molding, PLC and HMI software are used to implement functions that simply show operation data aggregated by facilities in real time. This has limitations for managers to make decisions related to product production other than viewing data. This study presents a method for upgrading the level of smart factories to suit the reality of small and medium-sized enterprises. By monitoring the data collected from the facility, it is possible to determine whether there is an abnormal situation by proposing an appropriate algorithm for meaningful decision-making, and an alarm sounds when the process is out of control. In this study, the function of HMI has been expanded to check the failure frequency rate, facility time operation rate, average time between failures, and average time between failures based on facility operation signals. For the injection molding industry, an HMI prototype including the extended function proposed in this study was implemented. This is expected to provide a foundation for SMEs that do not have sufficient IT capabilities to advance to the middle level of smart factories without making large investments.

사출성형공정은 열가소성 수지를 가열하여 유동상태로 만들어 금형의 공동부에 가압 주입한 후에 금형 내에서 냉각시키는 공정으로, 금형의 공동모양과 동일한 제품을 만드는 방법이다. 대량생산이 가능하고, 복잡한 모양이 가능한 공정으로, 수지온도, 금형온도, 사출속도, 압력 등 다양한 요소들이 제품의 품질에 영향을 미친다. 제조현장에서 수집되는 데이터는 양품과 관련된 데이터는 많은 반면, 불량품과 관련된 데이터는 적어서 데이터불균형이 심각하다. 이러한 데이터불균형을 효율적으로 해결하기 위하여 언더샘플링, 오버샘플링, 복합샘플링 등이 적용되고 있다. 본 연구에서는 랜덤오버샘플링(ROS), 소수 클래스 오버 샘플링(SMOTE), ADASTN 등의 소수클래스의 데이터를 다수클래스만큼 증폭시키는 오버샘플링 기법을 활용하고, 데이터마이닝 기법을 활용하여 품질예측을 하고자 한다.

In this paper, to improve the optical quality of aspherical plastic lenses for mobile use, the optimal molding conditions that can minimize the phase difference are derived using injection molding simulation, design of experiments, and machine learning. First, factors affecting the phase difference were derived using the design of the experiment method, and a data set was created using the derived factors, followed by the machine learning process. After predicting the model trained using the generated training data as test data and verifying it with the performance evaluation index, the model with the best predictive performance was the random forest model. Therefore, to derive the optimal molding conditions, random forests were used to predict 10,000 random pieces of data. As a result of applying the derived optimal molding conditions to the injection molding simulation, the phase difference of the lens could be reduced by 8.2%.

In this study, we used a numerical analysis program to study the molding conditions that affect the flow rate at the time of injection, using a spiral mold, which is mainly used for the evaluation of the flow rate of plastic resin. The mold temperature, melt temperature, and flow rate are composed of experimental factors. The three plastic forming factors were divided into five to six levels each. Since then, changes in the flow rate temperature were analyzed as the level of each forming factor increased. Experiments showed that all three forming factors increased the filling length of the spiral mold and the temperature of the flow front by a total of 34.53°C, melt temperatures increased the temperature of the flow front by a total of 34.53°C, the temperature increased by the flow rate was 23.5°C, and the temperature increased by the mold temperature was 1.99°C. It was shown that the melt temperature was the largest, followed by the flow rate and mold temperature. It was also possible to check the effect of plastic forming factors on the speed of the flow front.

Recently, the demand for reliability verification is increasing while designing and manufacturing molds using injection molding computer aided engineering(CAE). When performing flow analysis verification, a spiral mold is produced and compared with CAE. Because of the spiral shape, we needed a comparative evaluation with the flow distance of products with different forms. So, we compared the weight and flowed length using CAE. Variables are the change in the width of the spiral shape and the shape of the bar and plate. When the width of the spiral shape is 23mm rather than 15mm, the flow distance flows 30∼70mm more, with a maximum difference of 13%. As a result of comparing the spiral shape and the long square shape with the same width, the spiral shape had a flow distance of 60 to 105mm further, and a difference of up to 28% was found. As a result of comparing the plate shape and the spiral shape with a 15mm width product, the spiral shape has a flow distance of 310∼380mm further, and a difference of up to 82% is different.

The change in the open porosity of bulk graphite as a function of the uniaxial molding pressure during manufacturing is studied using artificial graphite powder. Subsequently, the graphite is impregnated to determine the effect of the open porosity on the impregnation efficiency and to improve the density of the final bulk graphite. Bulk graphite is manufactured with different uniaxial molding pressures after mixing graphite powder, which is the by-product of processing the final graphite products and phenolic resin. The bulk density and open porosity are measured using the Archimedes method. The bulk density and open porosity of bulk graphite increase as the molding pressure increases. The open porosity of molded bulk graphite is 25.35% at 30 MPa and 29.84% at 300 MPa. It is confirmed that the impregnation efficiency increases when the impregnation process is performed on a specimen with large open porosity. In this study, the bulk density of bulk graphite molded at 300 MPa is 11.06% higher than that before impregnation, which is the highest reported increase. Therefore, it is expected that the higher the uniaxial pressure, the higher the density of bulk graphite.

Plastic products molded by injection molding have become an essential element of our lives. In addition, plastics can replace parts that used to be metal in the past. Plastic molded products used as a part of a mechanical system require high precision. At the same time, the appearance quality of molded products is also an important evaluation factor. The appearance quality of a molded product is affected by injection molding conditions, plastic material fluidity, and the condition of the mold surface. In this study, the cause of the short shot of the dog house, which functions to assemble the plastic tailgate parts for automobiles, was analyzed. In order to solve the short shot problem of the dog house, the root thickness of the dog house, injection molding conditions, and fluidity of plastic materials were experimented. Through the injection molding experiment, it was found that when the dog house root thickness was increased from 0.8mm to 1.2mm, the filling amount of the doghouse part increased by 43% in experiment mold. These results were verified by injection molding analysis.

In this study, in order of to reflect the mold deformation in the injection molding process to design of mold, the mold deformation was analyzed by performing flow and structural analysis. The 5 inch LGP(light guide plate) mold, platen and tie bar were modeled and applied to the analysis. The result of melt pressure from flow analysis was extracted for use as boundary conditions acting on the mold surface in the structural analysis. In order to evaluate the accuracy of simulation analysis results, injection molding was performed under the process conditions of simulation. As a results, the mold deformation during injection molding tends to be similar that of injection pressure, and it is confirmed that it shows the behavior and properties of melt resins. Compared with the simulation and experiment, the error of the maximum mold deformation in the injection phase was 4.20%.

A combination of Polycarbonate (PC) material and Polymethylmethacrylate (PMMA), fabricated using an injection molding machine, has been investigated to determine its advantages, as studied in Ref. 1). This paper aims to investigate the optimization of PMMA/PC blend for both tensile yield strength and impact strength. Furthermore, interaction effects of process conditions on mechanical properties including tensile yield strength and impact strength of PMMA/PC blend by injection molding process are interpreted in this study. Tensile and impact specimens are designed following ASTM, type V, and are fabricated by injection molding process. The processing conditions such as melt temperature, mold temperature, packing pressure, and cooling time are applied; each factor has three levels. As a result, in comparison with optimization of separated responses, mechanical properties of PMMA/PC are found to decrease when optimizing both tensile and impact strengths simultaneously. The melt temperature is found to be the most significant interaction parameter with the mold temperature and packing pressure. In addition, there is more interaction between the mold temperature and cooling time. This investigation provides a useful understanding of the control of injection molding processing of polymer blends in optical application.