• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.46.2-46
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data [1]. Among many types of networks, a backpropagation neural network (BPNN) is the most widely used architecture. Many training variables are...

• Transactions of Materials Processing
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• v.17 no.3
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• pp.197-202
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• 2008

The control of springback is very important in sheet metal forming since springback affects the dimensional inaccuracy of product. The object of this study is to design the manufacturing process for the improvement of the performance of seat rail by DP780. The influence of process variables such as bend angle and pad force on the springback has been firstly investigated through the comparison between the results of FE-analysis and trial out for initial design based on designer's experience. The process variables of the initial design have been modified in order to improve the dimensional accuracy of seat rail from the prediction of springback by FE-analysis. It was shown from experiment that the improved design satisfied the required specifications such as the dimensional accuracy and the strength of seat rail.

• Journal of Surface Science and Engineering
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• v.53 no.4
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• pp.138-143
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• 2020

In this study, we produced a coil of micro-pattern that can be used for electromagnetic wave absorber, heating material, wireless charging, sensor, antenna, etc. by using electrochemical additive manufacturing method. Currently, it contains research contents for manufacturing a micro pattern coil having practicality through control of process control variables such as applied voltage, distance between electrode, and nozzle injection. Circulation of the electrolyte through the nozzle injection control can significantly contribute to improving the surface characteristics of the coil because of minimizing voltage fluctuations that may occur during the additive manufacturing process. In addition, by applying the pulse method in the application of voltage, the lamination characteristics of the plated body were improved, which showed that the formation of a fine line width plays an important role in the production of a micro pattern coil. By applying the pulse signal to the voltage application, the additive manufacturing characteristics of the produced product were improved, and it was shown that the formation of a fine line width plays an important role in the production of a micro pattern coil.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.6
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• pp.101-108
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• 2010

The drum clutch investigated in this study is formed in 5 forming steps, which are 1st deep drawing, 2nd deep drawing, restriking, embossing, and $Grob^{TM}$ processes. Dimensional accuracy of the final products greatly depends upon how much more accurate pre-form is manufactured in the previous forming processes before the $Grob^{TM}$ process. The deep drawing, restriking and embossing processes in which the pre-form is formed are very important and decisive steps. Thus in some cases, excessive strain by these operations causes dimensional inaccuracy and cracks initiated from the base and wall of the product. Process variables such as the punch shapes both of 1st and 2nd deep drawing, and punch angle were selected to evaluate the deformation characteristics. The optimum parameters were determined from forming simulations using commercial FEM codes, DEFORM and Tauchi method, specifically developed for metal forming simulation. Finally, experiments for the whole drum clutch forming processes were carried out to verify the optimized forming parameters and the analytical results.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.2
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• pp.49-56
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• 2003

As semi-solid forging (SSF) is compared with conventional casting such as gravity die-casting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally, SSF consists of reheating, forging, and ejecting processes. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power has large effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time for predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted from the reheating experiments. Results by neural network were in good agreement with those by experiment. Polynominal regression analysis was formulated using the test data from neural network. Optimum processing condition was calculated to minimize the grain size and solid fraction standard deviation or to maximize the specimen temperature average. Discussion is given about reheating process of row material and results are presented with regard to accurate process variables fur proper solid fraction, specimen temperature and grain size.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.503-506
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• 2004

Evolutionary operation is useful to improve on-line full-scale manufacturing processes by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for an evolutionary operation software called 'evolutionary optimizer'. The system design is based primarily on data flow diagram. Evolutionary optimizer consists of four modules: factorial design module, many variables module, mixture Production module, and mean/dispersion module.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.377-394
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• 2024

Purpose: To derive the optimal conditions for the Rubber based colloidal suspension manufacturing process, which made using a stirrer, to apply the mixture design method. Methods: We used two process component and one process variable Mixture design to derive the optimal conditions for the process. The response variables were selected for rotational viscometer measures which can represent Rubber based colloidal suspension quality. The input variables were selected as the values of rubber-organic solvent expressed in proportions as process components and stirring amount as a process variable which are controllable factors in the process. Results: Based on the results of the experiment, rubber and organic solvent and the interaction between stirring amount and rubber and the interaction between stirring amount and rubber and organic solvent were significant. Reproducibility of the regression model was confirmed by the observation that the values obtained from the reproducibility experiment fell within the confidence interval. Additionally, the model predictions were found to be in close agreement with the field measurements. Conclusion: In this study, a regression model was developed to predict the viscosity change of colloidal suspensions based on the proportion of rubber based colloidal suspension. The developed regression model can lead to improved product quality.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1996.06a
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• pp.75-86
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• 1996

Three-Dimensional finite element analysis is performed using PAM-STAMP for design evaluation of automotive back door inner panel die. Gravity process by blanks own weight, binder-wrap process, and drawing process in the forming operations are sequentially simulated with Virtual Manufacturing Method. The most valuable result in this research is that 3-D FEM analysis can be applied to the design evaluation of draw die in the die try-out, though effects of mesh size and drawbead resistance force on the numerical accuracy are much sensitive. For the intensive application to draw-die design and try-out, the experimental know-hows about the forming variables such as friction coefficient, punch velocity, drawbead force, etc are necessary.

• Journal of Biosystems Engineering
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• v.28 no.4
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• pp.325-334
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• 2003

Pinosylvin, a stilbenoid phytoalexin, is a health ingredient to be extracted from pine leaves. In this study, ultrasonication-assisted extraction process for manufacturing extracts with high content of pinosylvin from pine leaves was investigated. As process and system variables, ultrasonic power, sonication time and solvent ratio were selected. According to the experimental results, the effective yield of pinosylvin increased with the increase of ultrasonic power and sonication time and the decrease of solvent ratio. When the ultrasonic power of 2400 W/L was added to the solution of pulverized pine leaves of 8 g per 1 L of a solvent for 10 minutes, yield of extracts and purity, effective yield and concentration ratio of pinosylvin were 0.3166 g/g, 0.7247 mg/g, 0.2294 mg/g and 23.0, respectively.