• Tribology and Lubricants
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• v.18 no.5
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• pp.345-350
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• 2002

Recently, as the development of manufacturing technique on SMC(sheet molding compound), various numerical and experimental approaches to injection and compression molding have been investigated. Injection and compression molding, however, has so various cases with complicated boundary condition that it is difficult to analyze mold characteristics precisely. In addition, since a slight change in process variables can significantly change the resulting mold thickness, a proper design is important to compression molding process. Therefore, in this study, the effects of various parameters on compression molding process have been investigated using FEM(finite element method) to formulate the melt front advancement during the mold filling process. To verify the results of present analysis, they are compared with those of reference. The results show a strong effect of initial charge volume, injection time and pressure as a result of variations in the rectangular charge shape.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.11 s.242
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• pp.1445-1454
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• 2005

In order to minimize warping deformation which is an essential factor in the failure of injection molding parts, this study proposes an optimization design method fer determining design variables of injection molding parts. First, using a commercial package program for injection molding analysis, namely, Computer Aided Plastics Application(CAPA), we investigate the effects of parameters of injection molding process. Next, an optimum design process is established by interfacing CAPA to PQRSM embedded in EMD10S, a design framework developed by the conte. of innovative Design Optimization Technology(iDOT). PQRSM is a very efficient sequential approximate optimization algorithm. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the results of the optimum design is better than those of the initial design. It is believed that the proposed methodology can be applied to other injection molding design applications.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.144-150
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• 2005

Gas-assisted injection molding (GAIM) process is reducing the injection pressure during mold filling required as well as the shrinkage and warpage of the part and cycle time. Despite of these advantages, this process introduces new parameters and makes the application more difficult because the process interacts between gas and melt during injection molding process. Important GAIM factors that involved in this process include gas penetration design, locations of gas injection points, shot size, gas injection delay time as well as common injection molding parameters, gas pressure and gas injection time. In this study, the experiments were conducted to investigate effects of GAIM process variables on the gas penetration for PP and ABS moldings by changing gas injection point. Taguchi method was used fer the design of experiment. When the gas was injected at cavity's center, the most effective factor was shot size. When the gas was injected at cavity's end, the most effective factor was melt temperature. Injection speed was also an effective factor in GAIM process.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.2
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• pp.8-11
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• 2006

Gas-assisted injection molding (GAIM) process reduces the required injection pressure during mold filling stage as well as the shrinkage and warpage of the part and cycle time. Despite of these advantages, this process needs new parameters and makes the application more difficult because gas and melt interact during the injection molding process. Important GAIM factors involved in this process are gas penetration design, locations of gas injection points, shot size, delay time to inject gas as well as common injection molding parameters. In this study, the experiments are conducted to investigate effects of GAIM process variables on the gas penetration for PP (Polypropylene) and ABS (Acrylonitrile Butadiene Styrene) moldings by changing the gas injection point. Taguchi method is used for the design of the experiments. When the gas is injected at a cavity's center, the most effective factor is the shot size. When the gas is injected at a cavity's end, the most effective factor is the melt temperature. The injection speed is also an effective factor in GAIM process.

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.167-176
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• 2000

A study to analyze and solve problems of plastic injection molding experiment has presented in this paper. We have taken Taguchi's parameter design approach, specifically orthogonal array, and determined the optimal levels of the selected variables through analysis of the experimental results using S/N ratio.

• Transactions of Materials Processing
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• v.14 no.3 s.75
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• pp.233-238
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• 2005

Blow molding is divided into three categories, injection stretch blow molding, injection blow molding, and extrusion or direct blow molding. Extrusion blow molding has been studied experimentally to characterize the blowing behavior of parison. Blow conditions such as blowing temperature and cooling time were the experimental variables in this blowing experiment. Wall thickness of the lower part of blow molded sample was thicker than that of the upper part because of the sagging of parison during extrusion process. As temperature increases the wall thickness and the weight of blow molded sample decreased. No thickness variations in the blowing sample were observed according to the cooling time. The lower part of the sample showed high degree of crystallinity compare with the upper part of the sample. Thus the lower part of the sample was strong mechanically and structurally. It was recognized that the uniform wall thickness could not be obtained by only controlling the operational conditions. Parison variator should be introduced to get uniform wall thickness of parison and subsequently produce uniform wall thickness of blow molded product.

• IE interfaces
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• v.12 no.1
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• pp.26-31
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• 1999

Evolutionary Operation(EVOP) is a method for continuously monitoring and improving a full-scale process to get an optimal operating condition while production is under way. To avoid appreciable changes in the product quality characteristics only small changes are made in the levels of the process variables. One of the reasons why EVOP is not so popular is that people in charge of the EVOP is blamed when the EVOP does not produce good results. We present an EVOP procedure when prior information of the relationship between defect types and process variables is known. The procedure is illustrated with an injection molding case study.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.224-233
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• 1999

The quality of injection molded parts is affected by the variables such as materials, design variables of part and mold, molding machine, and processing conditions. It is difficult to consider all the variables at the same time to predict the quality. In this paper neural network was applied to analyze the relationship between processing conditions and volumetric shrinkage of part. Engineering plastic gear was used for the study, and the learning data was extracted by the simulation software like Moldflow. Results of neural network was good agreement with simulation results. Nonlinear regression model was formulated using the test data of 3,125 obtained from neural network, Optimal processing conditions were calculated to minimize the volumetric shrinkage of molded part by the application of RQP(Recursive Quadratic Programming) algorithm.

• Design & Manufacturing
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• v.15 no.1
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• 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.