• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.140.2-140
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• 2001

In this paper, we develop the system which inspects the metal mask using area scan camera and belu type xy-table and introduce its inspection algorithm. Thes whole area of the metal mask is divides into several inspection block and the sixe of a inspection block is decided by FOV(Field of View). To compare with the camera image of each block, the reference image is made by gerber file. The ratation angle of the metal mask is calculated through the linear equation that is substituted two end points of horizontal boundary of a specific hole in a camera image for. To calculate the position error caused by belt type xy-table, hough-transform using the distances among the holes in two images os used. The center of the reference image is moved as much as the calculated position error to be coincide with the camera image ...

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.146-153
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• 2003

Ultrasonic inspection of defects has been focused on the existence of defect in structural material and need has much time and expenses in inspecting all the coordinates (x, y) on material surface. Neural networks can have an application to coordinates (x, y) of defects by multi-point inspection method. Ultrasonic inspection modeling is optimized by neural networks Neural networks has trained training example of absolute and relative coordinate of defects, and defect pattern. This method can predict coordinates (x, y) of defects within engineering estimated mean error $\psi$.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.4
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• pp.199-203
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• 2006

This paper presents an ASER (Accelerated Soft Error Rate) integral model. The model is based on the facts that the generated EHP/s(electron hole pairs) are diminished after some residual range of the incident alpha particle, where residual range is a function of the incident angle and the capping layer thickness over the semiconductor junction. The ASER is influenced by the flux of the alpha particles, the junction area ratio, the alpha particle incident angle when the critical charge is same as the collected charge, and the sizes of the alpha source and the chip. The model was examined with 8M static RAM samples. The measured ASER data showed good agreement with the calculated values using the model. The ASER decreased exponentially with respect to the operational voltage. As the capping layer thickness increases up to $16{\mu}m$, the ASER increases, and after that thickness, the ASER decreases. The ASER increased as the depth of BNW increased from $0{\mu}m\;to\;4{\mu}m$. and then saturated. The ASER decreased as the node capacitance increased from 2fF to 5fF.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.5
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• pp.393-399
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• 2016

In this study, an experiment was conducted using a 3D scanner, commonly used in reverse engineering techniques, and the newly introduced CT measuring machine. The hole, width, and angle of specimens having various shapes were designated, the error rates in dimensional modelling generated during scanning with each device were compared, and the models were printed using a 3D printer. A secondary comparative analysis of the two printed specimens was conducted; the causes of dimension errors that occur during the printing process after scanning with each device and the differences associated with variation in shape were also analyzed. Based on the analysis results, the featured shape for each scanning application method and issues to consider in reverse engineering were presented, and the use of the CT measuring machine was recommended as a method to minimize error rates in dimensions and ensure efficient reverse engineering.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.5
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• pp.64-69
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• 2010

This study is the machining of fuel supply pipe used in large vessels. The fuel supply pipe of large vessels have effects to reduce engine exhaust because of common rail system and show excellent fuel efficiency so it is in the limelight as a vessel engine of next generation. At present, the shape of fuel supply pipe of common rail used for huge two-stroke & low-speed vessels is like a peanut hole so the second machining is necessary after the first machining. There is high error rate for machining and the materials waste caused by machining error is serious. Also, in this time the request for increasing the length of fuel supply pipe is suggested in the world market, it's judged that current methods will show higher error rate for machining. Therefore, the purpose of this study is to improve the machining process used originally. For that, the system controlling the process was developed as well as surface roughness and straightness which are evaluation items of fuel supply pipe were measured so that improved process can be observed in real time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.1004-1014
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• 2001

The fully automatic algorithm from initial finite element mesh generation to remeshing in two dimensional geometry is introduced using bubble packing method (BPM) for finite element analysis. BPM determines the node placement by force-balancing configuration of bubbles and the triangular meshes are made by Delaunay triangulation with advancing front concept. In BPM, we suggest two node-search algorithms and the adaptive/recursive bubble controls to search the optimal nodal position. To use the automatically generated mesh information in FEA, the new enhanced bandwidth minimization scheme with high efficiency in CPU time is developed. In the remeshing stage, the mesh refinement is incorporated by the control of bubble size using two parameters. And Superconvergent Patch Recovery (SPR) technique is used for error estimation. To verify the capability of this algorithm, we consider two elasticity problems, one is the bending problem of short cantilever beam and the tension problem of infinite plate with hole. The numerical results indicate that the algorithm by BPM is able to refine the mesh based on a posteriori error and control the mesh size easily by two parameters.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.3
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• pp.32-38
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• 2005

Electrical discharge machining is the method of using thermal energy by electrical discharge. Generally, if the material of workpiece has conductivity even though very hard materials and complicated shape which are difficult to cut such as quenching steel, cemented carbide, diamond and conductive ceramics, the EDM process is favorable one of possible machining processes. But, the process is necessarily required of finish cut and heat treatment because of slow cutting speed, no mirror surface, brittleness and crack due to the residual stress for manufactured goods. In this experimental thesis, the super EDM drilling was developed for high precision semiconductor die steel and for minimization of leadframe width. It was possible to development of EDM drilling machine for high precision semiconductor die with the electrode guide and its modelling and stress analysis. The development of electrode with the copper pipe type was conducted to drill the hole from the diameter of 0.1mm to 3.0mm with the error of from 0.02mm to 0.12mm. From the SEM and EDX analysis, the entrance of the EDM drill was found the resolidification of not only the component of tungsten but also the component of copper.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.2
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• pp.191-198
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• 2015

In this study, the flow characteristics at the ventilation holes was analyzed by using numerical method when carbon composite brake disk was rotated at a constant speed. In order to ensure the validity of the analysis results, grid dependency test was performed by considering the accuracy and appropriateness, and 4mm mesh size was selected for decrease of the maximum error rate 63.6%. As a result, the outside air flows in the clearance between the disk and shaft in case of B model. whereas, the outside air flows in the clearance or the outlet of the ventilation holes in case of A and C models. And also average static pressure at the outlet was changed depending on shape of the ventilation holes and rotational speed of the disk in case of A and C models. Besides, in the B model, intake air according to the clearance goes with side surface of ventilation hole, and so increased by mean velocity of 4.64m/s and mean pressure of 0.58pa in the ventilation hole outlet, in case of disk rotational speed of 146.21rad/s.