• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.221-224
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• 2003

In the open die forging process, it is difficult to optimize process parameters such as die shape, initial ingot size, feeding pitch, rotation angle and other process parameters in the operational environments. Therefore in this study, 3D finite element analysis has been performed to obtain optimal process condition for open die forging process. FEM analyses at various feeding pitches and rotation angles provide process conditions to make round bar having precise dimensional accuracy.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.625-632
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• 2012

Optimization of process parameters in polymer extrusion is an important task to reduce manufacturing cost. To determine the optimum values of the process parameters, it is essential to find their influence on the strength of polymer breathable thin film. The significance of six important process parameters namely, extruder cylinder temperature, extruder speed, extruder dies temperature, cooling roll temperature, stretching ratio, stretching roll temperature on breathable film strength of polymer extrusion was determined. Moreover, this paper presents the application of Taguchi method and analysis of variance (ANOVA) for maximization of the breathable film strength influenced by extrusion parameters. The optimum parameter combination of extrusion process was obtained by using the analysis of signal-to-noise ratio. The conclusion revealed that extruder speed and stretching ratio were the most influential factor on the film strength, respectively. The best results of film strength were obtained at higher extruder speed and stretching ratio.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.5-12
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• 2002

The grinding process has been mainly used fur finishing metal products as final machining stage. But chatter vibration and bum of a workpiece have a bad effect on the machined surface and should be detected in modern grinding process. This paper deals with a fault detection of the cylindrical plunge grinding process by power parameters. During the grinding process the power signals of an induced motor were sampled and used to determine the relationship between fault and change of power parameters. A neural network was used far detecting the grinding fault and an influence of power parameters to the grinding fault was analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.70-79
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• 1996

In off-line process optimization, process parameters are controlled such that the process is robust against changes in equipment conditions and incoming materials. The off-line methods, however, are not effective when the changes are so large that process parameters need to be adjusted. On-line control can respond to such large changes, but process uniformity has not been controlled on-line due to the difficulties in modeling. This paper is aimed at developing a new on-line control methodology where the uniformity is controlled effectively. The process variability is categorized based on the physical considerations, and the process parameters are classi- fied considering their effects on the categorized process variabilities. On-line control is performed with the properly selected process parameters so that robustness may not be degraded. The developed methodology is applied to the single wafer plasma etching processes, which resulted in both higher within-a-wafer uniformity and compens- ation of the incoming material non-uniformity.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.153-156
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• 1999

The technique is reported of identifying parameters in off-line process. The technique demands that closed-loop system consists of a reference and two-degree-of-freedom controllers (TDFC) in real process. A model process is the same as the real process except their parameters. Deviations are differences between the reference and the output of the plant or the model. The technique is based on minimizing identification error between the two deviations. The parameter differences between the plant and the model are characterized of mean value and of variance which are derived from the identification error. Consequently, the algorithm which identifies the unknown plant parameters is shown by minimizing the mean value and the variance, respectively, within double convergence loops. The technique is applied to course change of a ship. The plant deviation at the first trial is shown to occur in replacing the nominal parameters by the default parameters. The plant deviation at the second trial is shown to not occur in replacing the nominal parameters by the identified parameters. Hence, the identification technique is confirmed to be feasible in the real field.

• IE interfaces
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• v.21 no.2
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• pp.221-228
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• 2008

The injection molding process is able to produce high precision manufactures as a single process with fast speed. However, the prices of both the mold and the molding machine are expensive, and the single process is very complex and difficult to compose of the exact relationship between the process setting conditions and the product quality. Therefore, the quality of a molded product often depends on a skillful engineer's operations in the design of both parts and molds. In this paper, the relationship between the process conditions and the defectiveness is built for better manufactures under settings of the appropriate parameters, and so it can reduce the setup time in the injection molding process. Quality Function Deployment (QFD) provides severe defectiveness factors along with the related process parameters. Also, neural networks estimate the relationship between defective factors and process setting parameters, and lead to reduce the defectiveness of molded parts.

• Journal of Powder Materials
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• v.31 no.2
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• pp.137-145
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• 2024

In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.3
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• pp.209-216
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• 2008

The application of focused ion beam (FIB) depends on the optimal interaction of the operation parameters between operating parameters which control beam and samples on the stage during the FIB deposition process. This deposition process was investigated systematically in C precursor gas. Under the fine beam conditions (30kV, 40nm beam size, etc), the effect of considered process parameters - dwell time, beam overlap, incident beam angle to tilted surface, minimum frame time and pattern size were investigated from deposition results by the design of experiment. For the process analysis, influence of the parameters on FIB-CVD process was examined with respect to dimensions and constructed shapes of single and multi- patterns. Throughout the single patterning process, optimal conditions were selected. Multi-patterning deposition were presented to show the effect of on-stage parameters. The analysis have provided the sequent beam scan method and the aspect-ratio had the most significant influence for the multi-patterning deposition in the FIB processing. The bitmapped scan method was more efficient than the one-by-one scan type method for obtaining high aspect-ratio (Width/Height > 1) patterns.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.106-109
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• 2001

Process parameters of roller levelling process are intermesh of each roller, roller angle, roller arrangement and shape of rollers. Experimental optimization of these process parameters is very troublesome because of difficulties in evaluating the straightness of pipes to be levelled quantitatively. Finite element method can be a very efficient way to evaluate the straightness of the pipes and therefore to optimize the process. This paper is concerned with simulation and optimization of a roller levelling process. Process parameters of a 14-roller levller for aluminum T9 pipes are optimized with finite element method and Taguchi method. Parameters of significance in roller levelling process and their optimum are obtained.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.310-313
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• 2007

This paper covers finite element simulations to evaluate the bending limit of double pipe for tube-hydroforming. The tube-hydroforming process starts with a straight precut tube. The tube is often prebent in a rotary draw bending machine to fit the hydroforming tool. During the bending the tube undergoes significant deformation. So forming defects such as wrinkling, thinning and flattening are generated in the tube. Consequently we analyzed the effect of process parameters in rotary draw bending process and searched the optimized combination of process parameters to minimize the forming defects using orthogonal arrays. The characteristic to evaluate the effects of the process parameters is the bending angle which wrinkling is generated, we define the bending angle at that time as bending limit. Of many process parameters, the process parameters of the bending process such as gab between inner and outer tube, boosting force, dimensions of mandrel were analyzed. And we observed the deformation modes of bent double pipe at specific bending angle in each parameter combination.