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.

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.

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

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.

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%.

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.

This study was conducted for the purpose of suggesting a standard that can be used under ambient temperature by improving the low mechanical and thermal properties of ABS. PC was used as a filler, and post-curing conditions of the ABS/PC blend injection material were investigated. It was found that the ABS/PC blend injection material having a PC content of 20 wt.% or more showed little change in tensile properties at a temperature of 50°C, and a decrease in tensile properties of less than 10% at 80°C.

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.

When the product is taken out after the injection process, the surface of the product and the mold are attached and to separate them, it is necessary to consider the frictional force between the mold surface and the product surface. Therefore, to reduce the frictional force, a subtraction gradient for the rib shape is generally applied, and a lapping process is performed to improve the surface roughness of the rib shape surface of the processed mold. Therefore, research is needed to improve the surface roughness when processing the rib. In this study, slotting processing was applied to improve surface roughness when processing ribs. Slotting processing is a processing method that removes material through the feed motion of the tool, and processing is possible regardless of the aspect ratio of the processing shape. A slotting tool was developed for rib machining and a comparative experiment with electric discharge machining was performed. Also after processing, the surface roughness and processing time were compared and analyzed, and the improved surface roughness and fast processing time characteristics of the slotting processing compared to electric discharge processing were confirmed.

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.

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%.

Due to the development of the industry, the importance of injection molded products is increasing in the fields of electricity, electricity, automobiles, etc. Through this experiment, the weight change of molded products according to the injection speed change during injection molding was investigated. As a result of the experiment, a statistical program was used to confirm that the pi control method produced about 69.2% more weight deviation than the dpi method and to adopt the null hypothesis that the p-value was less than 0.05 Injection speed affected the molded product.