• Transactions of Materials Processing
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• v.14 no.7 s.79
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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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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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.

• Transactions of Materials Processing
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• v.13 no.2
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.1
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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.

• Journal of Korean Society for Quality Management
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• v.52 no.1
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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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• v.34 no.2
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• pp.289-295
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• 2010

Injection molding process is one of the most important methods to produce plastic parts with high efficiency and low cost. Today, injection molded parts have been increased dramatically the demand for high strength and quality applications. In this study, In-line skates are made of Al alloy and plastic materials to replace the frame for the optimization process is all about. I interpreted through mold design, Injection molding process that minimizes the runner and the gate dimension will determine the size and shape. Runner and gate dimensions of change based on availability of the product, I'll discuss the injection molding. This report investigates that the optimum injection molding condition for minimum of shrinkage. The FEM Simulation CAE tool, Moldflow, is used for the analysis of injection molding process.

• Design & Manufacturing
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• v.16 no.3
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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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.1
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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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.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.983-988
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• 2004

In the present study, the effects of orthogonal ribs on structural warpage during injection molding process were investigated. Basic ribbed models for the evaluation of degree of warpage were introduced and designed. Injection molds for these models are manufactured based on the full 3D CAD/CAM technology and specimens are prepared for experiment. Numerical analysis using commercial plastic injection molding analysis software was also performed to compare the results with experimental ones. The variations of materials and parameters such as injection time, mold temperature, melt temperature, holding time were considered in the present work. It was shown that orthogonal ribs have significant effect on the reduction of warpage during the injection molding process.