• 소성∙가공
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• 제30권3호
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• 소성∙가공
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• 제32권2호
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• 소성∙가공
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• 제32권6호
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• pp.352-359
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• 2023

Recently, independent suspension systems have been applied not only to passenger cars but also to large commercial vehicles. Therefore, the need for research to domestically produce such independent suspensions for large commercial vehicles is gradually increasing. In this paper, we conducted research on the manufacturing technology of the relay lever, which are integral components of independent suspension systems for large commercial vehicles. Our goal was to reduce the flash volume generated during the forging process. The shape variables of the initial billet were adjusted to find proper forming conditions that could minimize flash volume while performing product forming smoothly. Shape variables were set as input variables and the flash volume was set as an output variable, and simulations were carried out to analytically predict the volume of the flash area for each variable condition. Based on the data obtained through numerical simulations, a regression model and an artificial neural network model were used to develop a prediction model that can easily predict the flash volume for variable conditions. For the corresponding prediction model, a goodness of-fit test was performed to confirm a high level of fit. By comparing and analyzing the two prediction models, the high level of fit of the ANN model was confirmed.

• 한국정밀공학회지
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• 제15권8호
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• pp.17-25
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• 1998

Aluminum alloys have low density, relatively high strength and yield strength, good plasticity, good machinability, and high corrosion and acid resistance. Therefore, they are suitable for large containers for the food, chemical and other industries. Large containers are often bodies of revolution consisting of shell courses, stiffening rings, heads and other elements joined by annular welds. Larger containers have longer welds and require greater leak-tightness and higher weld mechanical properties. The LNG tank consists of aluminum plates with various sizes, so its construction should by divided by several sections. Moreover, each section has its own sub-section consisted of several aluminum plates. To guarantee the quality of huge LNG tank, therefore, the precise control of plate dimension should by urgently needed in conjunction with the appropriate selection of process parameters such as cutting speed, depth of cut, rotational speed and so on. In this paper, a manufacturing system was developed to implement automatic circular tracking in height direction and automatic circular interpolation in depth of cut direction. Also, the neural network based on the backpropagation algorithm was used to predict the cutting quality and motor load related with the life time of the developed system. It was revealed that the manufacturing system and the neural network could be effectively applied to the bevelling process and to predict the quality of machined area and the motor load.

• 대한추나의학회지
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• 제3권1호
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• pp.97-109
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• 2002

Objectives : Electroacupuncture(EA) has been suggested as a treatment for stroke rehabilitation. But whether, how much, by what mechanism and when it is effective has not been answered satisfactorily. Therefore it is important to critically review clinical trials and laboratory researches about EA for stroke rehabilitation. Subjectives : We researched various recent sources of EA for stroke rehabilitation such as medical journals and especially tried to review methodologically best randomized controlled trials(RCTs). Results and Conclusions : 1) EA increases brain plasticity, activity, blood flow and secretion of neuropeptides in CNS. 2) EA is significantly effective at the case that more than half of the neural motor pathway is reserved. 3) The acupoints, frequncy and intensity of EA should be determined by patient-specific symptoms of stroke. 4) More studies is needed for merdian functions for stroke rehabilitation.

• 소성∙가공
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• 제14권9호통권81호
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• pp.791-797
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• 2005

Extensive width reduction of slabs is an important technology for achieving continuous production between the steelmaking and hot rolling processes. However, the vertical horizontal rolling process has many disadvantages, e.g., large width deviations and less efficient width reduction. This study was carried out to investigate the deformation of slab by sizing press with two steps die. To do it, dog-bone and camber are discussed in width sizing process considering the deformation behavior according to the deviation of anvil velocity and the deviation of initial slab temperature. In this paper, the various causes of the sizing press phenomena are mentioned for the purpose of understanding of rolling conditions. As a result, the optimal anvil shape having a minimum-forming load is obtained by FE-simulation and ANN.

• 소성∙가공
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• 제24권5호
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• pp.332-339
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• 2015

In the current paper, a cold forging sequence was developed to manufacture a precisely cold forged H/Lower, which is used as the air back unit in commercial automobiles. The preform shape of the H/Lower influences the dimensional accuracy and stiffness of the final product. The shape factor (SF) ratio and shape of the tools are considered as the design parameters to achieve adequate backward extrusion height and maintain appropriate thickness variations. The optimal conditions of the design parameters were determined by using an artificial neural network (ANN). To experimentally verify the optimal preform and tool shapes, the experiments of the backward extrusion of the H/Lower were executed. The process design methodology proposed in the current paper, can provide a more systematic and economically feasible means for designing the preform and tool shapes for cold forging.

• BMB Reports
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• 제53권10호
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• pp.500-511
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• 2020

14-3-3 proteins are mostly expressed in the brain and are closely involved in numerous brain functions and various brain disorders. Among the isotypes of the 14-3-3 proteins, 14-3-3γ is mainly expressed in neurons and is highly produced during brain development, which could indicate that it has a significance in neural development. Furthermore, the distinctive levels of temporally and locally regulated 14-3-3γ expression in various brain disorders suggest that it could play a substantial role in brain plasticity of the diseased states. In this review, we introduce the various brain disorders reported to be involved with 14-3-3γ, and summarize the changes of 14-3-3γ expression in each brain disease. We also discuss the potential of 14-3-3γ for treatment and the importance of research on specific 14-3-3 isotypes for an effective therapeutic approach.

• International Journal of Oral Biology
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• 제40권4호
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• pp.161-166
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• 2015

Neuronal activities of taste-responsive cells in the nucleus of the solitary tract (NST) are affected by various physiological factors, such as blood glucose level or sodium imbalance. These phenomena suggest that NST taste neurons are under the influence of neural substrates that regulate nutritional homeostasis. In this study, we reviewed a series of in vivo electrophysiological investigations that demonstrate that forebrain nuclei, such as the lateral hypothalamus or central nucleus of the amygdala, send descending projections and modulate neuronal activity of gustatory neurons in the NST. These centrifugal modulations may mediate plasticity of taste response in the NST under different physiological conditions.