In this study, We designated the injection molded plug housing for charging electric vehicles as a research subject. And we analyzed the effect of Rib design on the quality of injection molded products. First, we used the Taguchi method to derive optimal conditions for rib design. The factors were set as the Thickness of the rib, the Height of the rib, and the Radius of the rib. Each factor consisted of 5 levels and generated conditions for a total of 125. We performed an injection molding analysis and confirmed significant factors affecting the deformation of injection molded products through ANOVA. Based on this, the 25th design detail was selected as the optimal condition. In addition, We compared the results of the molding analysis with the molded products that did not design ribs. We confirmed that the molded product designed with ribs under optimal design detail improved the deformation amount by 22.22% and the residual stress by 8.35%, compared to the molded product not designed with ribs.

For a plastic diffusion lens to uniformly diffuse light, it is important to minimize deformation that may occur during injection molding and to minimize deformation. It is essential to control the injection molding condition precisely. In addition, as the number of meshes increases, there is a limitation in that the time required for analysis increases. Therefore, We applied machine learning algorithms for faster and more precise control of molding conditions. This study attempts to predict the deformation of a plastic diffusion lens using the Decision Tree regression algorithm. As the variables of injection molding, melt temperature, packing pressure, packing time, and ram speed were set as variables, and the dependent variable was set as the deformation value. A total of 256 injection molding analyses were conducted. We evaluated the prediction model's performance after learning the Decision Tree regression model based on the result data of 256 injection molding analyses. In addition, We confirmed the prediction model's reliability by comparing the injection molding analysis results.

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%.

Recently, 3D printing has been actively studied. A representative material in this 3D printing technology is plastic, and PLA, an eco-friendly material, is widely used. FDM is widely used as a way to output these PLA materials. However, this method lacks mechanical properties compared to injection-molded products as it is a method of stacking materials by melting. Therefore, in this study, using an FDM-type 3D printer, a tensile test was performed after printing a tensile specimen with PLA filament with the layer angle and layer density as control factors. After that, changes in tensile properties according to the layer angle and density were compared and evaluated. As a result, to improve the tensile strength, the layer density had to be considered, and to improve the elastic modulus, both the layer angle and the layer density had to be considered.

This paper analyzed the correlation between injection molding factors through correlation analysis. In addition, the decision-tree model, which is a white box model with excellent explanatory power, was used to obtain optimal molding conditions that satisfy multiple constraint conditions. First, 243 data to be used in the experiment were created through a full factorial design. Second, a correlation analysis was conducted to understand the correlation. Third, to verify the decision-tree model, the prediction performance was evaluated using RMSE. As a result, good prediction performance was confirmed. A decision-tree experiment analysis was conducted. As a result of the progress, the same results as the correlation analysis were derived. Based on the previous analysis results, optimal molding conditions were applied to CAE. As a result, the amount of deformation in the multi-cavity could be improved by about 1.1% and 2.72% while satisfying the constraint.

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.

Additive manufacturing technology, 3D printing, has been applied to various industrial fields. This production method is a production method with less material, cost and time savings, and less restrictions in shape, and is also making a leap forward in the field of eco-friendly product production. In particular, FDM (fused depositon modeling) method of extrusion lamination manufacturing is widely applied in products and medical fields. And as an alternative to mold manufacturing, it is widely used in manufacturing plastic products and parts. Therefore, this paper quantitatively and qualitatively analyzes the mechanical properties according to the processing factors of the specimen through the processing of the ABS tensile specimen printed by the FDM type 3D printer and derives the optimum value.

This study wanted to optimize the radiator tank's deformation assembled on the automotive engine block. Among the experimental planning methods, the Taguchi method was used to find optimal molding conditions to minimize plastic covers' deformation. The four main factors used in the Taguchi method were selected as the main factors: resin temperature, pressure time, coolant temperature, and cooling time. The number of cycles for each factor was divided into five stages, and a total of 25 experiments were conducted. The experiment used the Moldflow program, an injection molding analysis program. The maximum deformation obtained under the existing molding conditions was about 1.318mm. Still, the deformation of the mold applied with the optimal molding conditions obtained using the Taguchi method was approximately 1.273mm, which showed that the maximum deformation was reduced by 3.4% compared to the existing molding conditions.

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 longest process in the injection molding process is the cooling process of the molded product. Therefore, shortening the cooling time is key to reducing the injection molding cycle time. For fast cooling time, the production of conformal cooling channels using metal 3D printing instead of the conventional linear cooling channels is continuously increasing. In this study, the cooling effect of the conventional linear cooling channel application and the conformal cooling channel application using metal 3D printing was compared in the design of the back cover molding mold of the circulator that has been widely used recently. The comparison of the cooling effect was based on the mold temperature and the molded product temperature for a certain period of time after completion of molding. It was confirmed that the time required to eject from the mold with the conformal cooling channel to the ejecting temperature of the molded product was reduced by 28.7%, and the maximum temperature of the mold was also reduced by 40%.

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.

Excellent plastic moldings is possible through optimization of many molding parameters. In particular, the deformation of a plastic part is affected by various factors during molding. Therefore, it is very important to select the optimum molding conditions that minimize the deformation of the molded part. Experimental design is used to select optimal molding conditions. In this study, the molding conditions were selected to minimize the deformation of the electric plastic plug of the electric vehicle using the Taguchi method in the experimental design method. Using the Taguchi Method, we found that the deformation of the plug moldings was reduced by about 7.2% compared to before optimization.

The cooling process in the injection molding requires the longest time. Therefore, a lot of studies have been conducted to reduce the cooling time. In particular, studies on conformal cooling channels using 3D printing are actively being conducted. In this study, the effect of the conformal cooling channel considering the hood shape instead of the conventional linear cooling channel was investigated by injection molding analysis. In the analysis results, when the conformal cooling channel was applied, the length deformation of the molded product was reduced by about 33% and the circular deformation of the hood assembled on the lens was reduced by about 7.1㎛.