• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.10
• /
• pp.768-773
• /
• 2003

Injection molding has been widely used for the mass production of a plastic product. With the development of the relative technique, various injection molding techniques have been developed and we could get more precise plastic product. The temperature of a melting resin is an important factor in the injection molding and this temperature has direct influence on the quality of a plastic product. In the present injection molding machine, the deriation of a temperature controlled by PID control method is within 2$^{\circ}C$ in the injection molding machine but PID control method takes too much time to stabilize after preheating and its overshoot is so big. We applied fuzzy control to alleriate the problem. In this research, we experimented the fuzzy temperature control with the usage of PC based PLC.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.7
• /
• pp.2963-2968
• /
• 2011

Plastic injection products have been widely used in various electronic appliances and high-tech commodities. However, plastic injection product manufacturers have to spare no efforts to shorten new product development period to introduce new products into the market ahead of other competitors, gaining competitiveness and satisfying customers. The manufacturers cannot only get big target market share rapidly but also the advantage of leading the product price in order to survive in highly competitive market. This paper proposes the cost estimation approach of feature-based plastic injection products by using hybrid artificial neural network and genetic algorithm. The proposed method is to dramatically simplify and shorten the complex conventional cost estimation procedures and the requested computation parameters of plastic injection products. The case study demonstrates the efficiency and effectiveness of the proposed model in solving the cost estimation problem of plastic injection products at the development stage.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.14 no.2
• /
• pp.87-92
• /
• 2015

The injection molding process is widely used in the production of most plastic products. In order to make metal-colored plastic products like those found in modern luxury home alliances, metallic pigments are mixed with a basic resin material for injection molding. However, process control for metal-colored plastic products is extremely difficult due to the non-uniform melt flow of the metallic resin pigments. In this study, the effect of process parameters on the quality of a metal-colored plastic product is evaluated. A rapid mold cooling method using a compressed cryogenic fluid is also proposed to decrease the content of undesired compounds within the plastic product.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.8 no.2
• /
• pp.53-59
• /
• 2009

The analysis of injection molding process by CAE is widely used in development of plastic products. That comes from the fact that CAE analysis can reduce trial and error based on optimized design. On this study, by use of MOLDFLOW, the causes of product defects were found and solved by trade-off study. CAE analysis includes Flow-Cool-Warpage Analyses and finally a new mold-die design with better product quality was suggested. On injection molding of round-shaped plastic fan, new mold-die system with 4-tunnel gates located on the edge of a fan disc shows better quality rather than pin-point gate located on the center of a disc. That was effective in terms of flow mark removal and flatness improvement of the product.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.12
• /
• pp.5709-5715
• /
• 2012

Injection molding product is commonly used for reducing the weight of automotive vehicle, and door regulator guide rail with plastic material is also made by injection molding process. In order to improve the injection molding performance of plastic regulator guide rail, optimal molding condition is suggested by numerical simulation and DOE after obtaining the sensitivity of parameters for regulator rail manufacturing on warpage and fill time. Furthermore, multi direct gate method and optimal cooling circuit are introduced to get the uniform temperature distribution and better cooling efficiency in molding product. The effect of the proposed design on the injection molding performance is verified by the test of prototype of plastic regulator guide rail.

• Transactions of Materials Processing
• /
• v.18 no.2
• /
• pp.177-182
• /
• 2009

A work of thin-walled outsell injection molding technology for a plastic part of moldframe applicable in a display product was performed in the present study. The thin-walled plastic part is one of the core parts in the display product, which supports and protects a light guide plate and back light unit from external environmental conditions. It globally has the shape of rectangular and surrounds the light guide plate and back light unit for each class of inch, however, the cross section of the part is not clear to define the thickness. This causes the difficult problem of injection molding itself for the part. Moreover, a metal outsell part makes a difficult problem in injection molding over it. Because the mold temperature control of the parts are not uniform in thickness direction due to the metal part. A careful injection melding analysis and injection mold design from the analysis results have to be proceeded to obtain a production of precision moldframe. Therefore, optimization for injection molding process and analysis of warpage characteristics were studied. Consequently, it was possible from the presented virtual manufacturing process that the manufacturing of precision thin-walled outsell moldframe.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.16 no.5
• /
• pp.105-112
• /
• 2017

In this study, plastics were deformed after molding due to the characteristics of the material. The Taguchi experimental design method was utilized to find the molding conditions that minimized deformation of the plastic bezel to be assembled in an automotive steering wheel. The injection molding conditions applied to the experimental design method are the melt temperature, cavity plate coolant temperature, core plate coolant temperature, and cooling time. Each condition was divided into five levels, and a total of 25 experiments were planned. However, instead of performing 25 actual molding experiments, the injection molding analysis was performed using the Moldflow program, and the deformation values for each molding analysis were obtained. The optimal molding conditions were obtained from these deformation values. The actual injection molding experiment using optimal molding conditions was compared with the deformation amount of the current molded product. The deformation was measured using a precise 3D scanner. The deformation amount of the molded product under optimal molding conditions was 16.1% lower than the deformation amount of the current molded product.

• Design & Manufacturing
• /
• v.17 no.2
• /
• pp.62-69
• /
• 2023

In this study, we conducted a study on the optimization of injection molding conditions to minimize deformation of plastic product. The charging management system housing of the vehicle was selected as the research subject. Melting temperature, cooling temperature, packing time, and packing pressure were selected as the main factors expected to affect the deformation of molded products. Each main factor was divided into 5 levels. Optimization of injection molding conditions to minimize deformation was performed using the Taguchi Method. We performed an analysis of variance (ANOVA) to identify significant factors affecting the deformation of plastic product. In order to select injection molding conditions that minimize deformation of plastic products, injection molding analysis was additionally performed for insignificant factors. We then compared the deformation of the molded part before and after optimization. As a result of comparing the injection analysis results of the basic conditions and the injection analysis results of the optimal conditions, it was confirmed that the amount of deformation after optimization was improved by about 10.9%.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2005.09a
• /
• pp.126-131
• /
• 2005

In the present study, warpage characteristics of mobile phone cover through injection molding process were investigated by using design of experiments. Warpage in plastic injection molding process has a significant effect on quality of product. Effects of injection time, packing pressure, packing time, mold temperature ana melt temperature on warpage of mobile phone cover were considered by CAE analysis and experiment with Taguchi method. The degree of warpage for the injection molded product was measured by using three dimensional CMM. It was shown that temperature parameter has more significant effect on the warpage of mobile phone cover than pressure parameter.

• Design & Manufacturing
• /
• v.18 no.2
• /
• pp.64-73
• /
• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.