• 소성∙가공
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• 제14권7호
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• pp.624-627
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• 2005

In this paper, we investigated the possibility of replicating patterned media by nano-injection molding process with a metallic nano-stamper. The original nano-master was fabricated by I-beam lithography and ICP etching process. The metallic nano-stamper was fabricated using a nanoimprint lithography and nano-electroforming process. Finally, the nano-patterned substrate was replicated using a nano-injection molding process without additional etching process. The replicated patterns using nano-injection molding process were as small as 50nm in diameter, 150nm in pitch, and 50nm in depth.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2005년도 춘계학술대회 논문집
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• pp.60-63
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• 2005

In this paper, we investigated the possibility of replicating patterned media by nano-injection molding process with a metallic nano-stamper. The original nano-master was fabricated by E-beam lithography and ICP etching process. The metallic nano-stamper was fabricated using a nanoimprint lithography and nano-electroforming process. Finally, the nano-patterned substrate was replicated using a nano-injection molding process without additional etching process. The replicated patterns using nano-injection molding process were as small as 50 nm in diameter, 150 nm in pitch, and 50 nm in depth.

• 소성∙가공
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• 제13권2호
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• pp.180-187
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• 2004

A computer code was developed to simulate the filling stage of the injection/compression molding process by a finite element method. The constitutive equation used here was the compressible Leonov model. The PVT relationship was assumed to follow the Tait equation. The flow-induced birefringence was related to the calculated flow stresses through the linear stress-optical law. Simulations of a disk part under different process conditions including the variation of compression stroke and compression speed were carried out to understand their effects on birefringence variation. The simulated results were also compared with those by conventional injection molding.

• 한국자동차공학회논문집
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• 제19권1호
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• pp.66-72
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• 2011

Injection pressure is an important factor in filling procedure for injection molded parts. In addition, weldlines should be avoided to successfully produce injection molded parts. In this study, we optimally obtained injection molding process parameters that minimize injection pressure. Then, we determined the thickness of the part to avoid weldlines. To solve the optimization problem proposed, we employed MAPS-3D (Mold Analysis and Plastics Solution-3 Dimension), a commercial CAE tool for injection molding analysis, and PIAnO (Process Integration, Automation, and Optimization) as a commercial PIDO (Process Integration and Design Optimization) tool. We integrated MAPS-3D into PIAnO, automated the analysis and design procedure, and performed optimization by employing PQRSM (Progressive Quadratic Response Surface Method) equipped in PIAnO. We successfully obtained optimization results, which demonstrates the effectiveness of our design method.

• 품질경영학회지
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• 제52권1호
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Journal of Advanced Marine Engineering and Technology
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• 제34권2호
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• pp.289-295
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• 2010

플락스틱 재료를 사용한 가공법 중에서 가장 보편적인 가공법이 사출성형이다. 플라스틱 재료의 활용도는 지속적으로 증가하고 있으며, 신소재 등의 개발로 그 적용범위 또한 확대되고 있다. 사출성형에서 수축현상은 수지의 종류, 즉 결정성 수지인지 비결정성 수지인지에 따라 크게 다르게 나타나며 사출성형시의 운전조건에 따라서도 다르다. 본 연구에서는 Al합금으로 제작되고 있는 인라인 스케이트의 프레임을 플라스틱 재료로 대체하기 위한 최적화 공정에 관한 것이다. 금형설계 전 해석을 통하여 성형공정이 최소화되는 런너와 게이트의 치수와 형상을 결정하겠다. 런너와 게이트의 치수 변화에 따른 제품의 사출성형성을 알아보겠다. 본 연구의 시뮬레이션에서는 사출성형해석용 소프트웨어인 Moldflow를 이용해서 해석을 수행하였다.

• Design & Manufacturing
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• 제16권3호
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• pp.50-57
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• 2022

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

• 한국기계가공학회지
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• 제14권1호
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• pp.44-50
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• 2015

This paper presents a heuristic process-optimization procedure for minimizing warpage in injection-molded parts based on the dynamic robust design methodology. The injection molding process is known to have intrinsic variations of its process conditions due to various factors, including incomplete process control facilities. The aim of the robust design methodology advocated by Taguchi is to determine the optimum design variables in a system which is robust to variations in uncontrollable factors. The proposed procedure can determine the optimal robust conditions of injection molding processes at a minimum cost through a trade-off strategy between the degree of warpage and the packing time.

• Design & Manufacturing
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• 제18권2호
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• pp.35-40
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• 2024

Injection molding is a process widely used across various industries for molding plastics, and it is the most commonly applied process in root industries utilizing molds. Among the different types of injection molding, insert injection molding, where busbars are used as inserts, is increasingly being applied in the electric vehicle industry. However, currently, the insert injection molding process is manually performed, with workers placing insert components by hand before injection molding. This results in issues related to productivity, safety, and quality. Additionally, there is a growing demand for automation of such production lines due to hazardous working conditions, economic difficulties in the manufacturing industry, and the decline in the labor force caused by an aging population. This study focuses on the application of an automated system for the insert injection molding process used in electric vehicles. The development of an automated system for the transport and insertion of insert components, as well as the inspection and stacking processes after injection, has resulted in over a 25% improvement in productivity and more than a 27% reduction in defect rates.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.316-319
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• 2002

Injection mold is a manufacturing process used to produce parts of complicated shape at a low cost. Many factors affect the quality of injection molded part during injection molding process. A study on the optimization of injection mold is progressed by using a simulation software like Moldflow. Filling, packing and cooling phases of injection molding processes are analyzed according to the mold design considering the shrinkage of molded part, the degree of filling rate and the wearing of a mold. Taguchi method is applied to analyze the significance of processing parameters and the dynamic characteristics according to the variation of processing parameters. From the results, the mold temperature and packing pressure influenced strongly the shrinkage of injection molded part.