• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.1
• /
• pp.69-74
• /
• 1998

In order to monitor the tool wear in milling operation, cutting force is measured as the tool wear increased. The digital signal processing methods are used to detect the tool wear . As AR parameter extract the feature of tool wear , it can be used as input parameter of pattern classifier. The FFT monitor the tool wear exactly , but it can not do real time signal processing. The band energy method can be used to real time monitoring of tool wear ,but int can degrade the exact monitoring.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.7
• /
• pp.585-590
• /
• 2014

Recently, monitoring systems, such as POP, take a core role in scheduling or planning of manufacturing facilities for production, maintenance, and so on. Such monitoring systems require functionalities for real-time parameter monitoring and controlling to maximize efficiency of facilities. However, vendors usually do not provide internal communication protocols or interface to access the machine controller. Therefore, the values of parameters related to machine operations and controls cannot be easily accessed from external devices. In this paper, we propose an interface methodology for a real-time monitoring and controlling of injection molding machine parameters such as user input parameters, embedded sensor data and injection molding status information.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.31 no.2
• /
• pp.165-173
• /
• 2011

The acoustic emission(AE) technique is a well established method to carry out structural health monitoring(SHM) of large structures. However, the real-time monitoring of the crack growth in the roller coaster support structures is not easy since the vehicle operation produces very large noise as well as crack growth. In this investigation, we present the waveform parameter filtering method to classify acoustic sources in real-time. This method filtrates only the AE hits by the target acoustic source as passing hits in a specific parameter band. According to various acoustic sources, the waveform parameters were measured and analyzed to verify the present filtering method. Also, the AE system employing the waveform parameter filter was manufactured and applied to the roller coaster support structure in an actual amusement park.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.157-166
• /
• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.19 no.2
• /
• pp.16-22
• /
• 2011

It is recognized that the control surface fault is detected by monitoring the value of the coefficients due to the control surface deviation. It is found out the control surface stuck position by comparing the trim value with the reference value. To detect and isolate the fault, two mixed methods apply to the real-time parameter estimation and similarity measure. If the scatter of aerodynamic coefficients for the fault and normal are closing nearly, fault decision is difficult. Applying similarity measure to decide for fault or not, it makes a clear and easy distinction between fault and normal. Low power processor is applied to the real-time parameter estimator and computation of similarity measure.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.4
• /
• pp.410-418
• /
• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• Journal of Astronomy and Space Sciences
• /
• v.18 no.2
• /
• pp.129-136
• /
• 2001

The accuracy of GPS applications is heavily dependent on the satellite ephemeris and earth orientation parameter. Specially applications like as the real time monitoring of troposphere and ionosphere require real time or predicted ephemeris arid earth orientation parameter with very high quality. IGS is producing IGS ultra rapid product called IGU for real time applications which includes the information of ephemeris and earth orientation. IGU is being made available twice everyday at 3:00 and 15:00 UTC arid covers 48 hours. The first 24 hours of it are based on actual GPS observations and the second 24 hours extrapolated. We will construct the processing strategy for yielding ultra rapid product and demonstrate the propriety through producing it using 48 hours data of 32 stations.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.11 no.1
• /
• pp.1-5
• /
• 2011

To enter next process control, numerous approaches, including run-to-run (R2R) process control and fault detection and classification (FDC) have been suggested in semiconductor manufacturing industry as a facilitation of advanced process control. This paper introduces a novel type of optical plasma process monitoring system, called plasma eyes chromatic system (PECSTM) and presents its potential for the purpose of fault detection. Qualitatively comparison of optically acquired signal levels vs. process parameter modifications are successfully demonstrated, and we expect that PECSTM signal can be a useful indication of onset of process change in real-time for advanced process control (APC).

• Proceedings of the Korean Vacuum Society Conference
• /
• 2013.02a
• /
• pp.342-343
• /
• 2013

AlSb is a promising material for optical devices, particularly for high-frequency and nonlinear-optical applications. And AlSb offers significant potential for devices such as quantum-well lasers, laser diodes, and heterojunction bipolar transistors. In this work we study molecular beam epitaxy (MBE) growth of an unstrained AISb film on a GaAs substrate and identify the real-time monitoring capabilities of in situ spectroscopic ellipsometry (SE). The samples were fabricated on semi-insulating (0 0 1) GaAs substrates using MBE system. A rotating sample stage ensured uniform film growth. The substrate was first heated to $620^{\circ}C$ under As2 to remove surface oxides. A GaAs buffer layer approximately 200 nm- thick was then grown at $580^{\circ}C$. During the temperature changing process from $580^{\circ}C$ to $530^{\circ}C$, As2 flux is maintained with the shutter for Ga being closed and the reflection high-energy electron diffraction (RHEED) pattern remaining at ($2{\times}4$). Upon reaching the preset temperature of $530^{\circ}C$, As shutter was promptly closed with Sb shutter open, resulting in the change of RHEED pattern from ($2{\times}4$) to ($1{\times}3$). This was followed by the growth of AlSb while using a rotating-compensator SE with a charge-coupled-device (CCD) detector to obtain real-time SE spectra from 0.74 to 6.48 eV. Fig. 1 shows the real time measured SE spectra of AlSb on GaAs in growth process. In the Fig. 1 (a), a change of ellipsometric parameter ${\Delta}$ is observed. The ${\Delta}$ is the parameter which contains thickness information of the sample, and it changes in a periodic from 0 to 180o with growth. The significant change of ${\Delta}$ at~0.4 min means that the growth of AlSb on GaAs has been started. Fig. 1b shows the changes of dielectric function with time over the range 0.74~6.48 eV. These changes mean phase transition from pseudodielectric function of GaAs to AlSb at~0.44 min. Fig. 2 shows the observed RHEED patterns in the growth process. The observed RHEED pattern of GaAs is ($2{\times}4$), and the pattern changes into ($1{\times}3$) with starting the growth of AlSb. This means that the RHEED pattern is in agreement with the result of SE measurements. These data show the importance and sensitivity of SE for real-time monitoring for materials growth by MBE. We performed the real-time monitoring of AlSb growth by using SE measurements, and it is good agreement with the results of RHEED pattern. This fact proves the importance and the sensitivity of SE technique for the real-time monitoring of film growth by using ellipsometry. We believe that these results will be useful in a number of contexts including more accurate optical properties for high speed device engineering.

• Smart Structures and Systems
• /
• v.23 no.4
• /
• pp.359-371
• /
• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.