• Journal of the Korean Society of Industry Convergence
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• v.6 no.4
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• pp.379-386
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• 2003

The goal of this research is to develop a Heel/Side Laster and control GUI(Graphic User Interface) for adaptive manufacturing. For this purpose, we have analyzed the working sequences of heel/side laster, and developed a control program that will facilitate the machineries with functions that are suitable for adaptive manufacturing. We also made it possible to modify the gluing path with simple manipulation of CAD data. By providing a user-friendly GUI, we made it possible for unskilled workers use the system without difficulty. In addition, we have developed a flexible environment where the already available CAD data can be modified and saved with ease. Automatic feeding and path control algorithms for thermoplastic cement were also implemented. By using the Heel/Side Laster for adaptive manufacturing, we are able to achieve increased productivity and work efficiency while improving the quality of the product with self-diagnosis and fine adjustment function.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.51-60
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• 2004

Due to the improvements in circuit design and manufacturing technique, the complexity of a circuit is growing. Since the complexity of a circuit causes high frequency of faults, it is very important to locate faults for improvement of yield and reduction of production cost. But unfortunately it takes a long time to find sites of defects by e-beam proving if the physical level. A fault diagnosis algorithm in the Sate level has meaning to reduce diagnosis time by limiting fault sites. In this paper, we propose an efficient fault diagnosis algorithm in the logical level. Our method is hybrid fault diagnosis algorithm using a new fault dictionary and additional fault simulation which minimizes memory consumption and simulation time.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.267-270
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• 2021

In order to diagnose a vehicle, it is achieved by collecting diagnostic data within the ECU or between ECUs and managing the diagnostic data by utilizing various communication methods through an electronic device composed of an ECU(Electronic Control Unit), which is an automotive electronic device. As communication methods, LIN, CAN, FlexRay are mainly used. Recently, wired/wireless communication is being used based on Ethernet. In order to perform vehicle diagnosis, it is necessary to know the diagnosis code generated by the ECU and to collect diagnosis data using diagnosis communication. In addition, diagnostic data can be managed from the ECU only when the application software required for vehicle diagnosis is configured. If many automobile manufacturers are manufacturing ECUs based on the AUTOSAR standard, which is an automotive electronic standard, the software structure is also configured to be applied according to the standard. In this paper, we understand the vehicle diagnosis communication method of the AUTUSAR standard, study the configuration and processing method of diagnosis data, and study the contents of software components, diagnosis communication, and diagnosis event processing.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.59-70
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• 2000

To keep enterprise's competitiveness on condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only production and maintenance but also related industrial safety. As we analyze in the surveys for the maintenance management of domestic enterprises and the causes of Industrial accident, there will be necessity of drawing up countermeasures for prevention of industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, this study studied the safety information system, maintenance management information system, and the machinery condition diagnosis technique by using of the knowledge-based system under the internet environment. This web based knowledge-based diagnosis system can easily provide not only the knowledge of expert about deterioration phenomenon of industrial robot, but also the knowledge of relating safety and facility on everywhere, everytime. Therefore, when we use this system, it is expected to improve the efficiency of business processes in the production and safety.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.125-130
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• 2001

Diagnosis or measurements using Infrared thermo-image hasn t been available. A quick diagnosis and thermal analysis can be possible when that kind of system is introduced to the investigation of each part. In this study, Infrared Camera, Thermo-vision 900 was used in order to investigate. Infrared Camera usually detects only Infrared wave from the light in order to illustrate the temperature distribution. Infrared diagnosis system can be applied to various field. Also, it is more effective to analyze temperature distribution on the machining center main-axis process.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.708-713
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• 1997

A remote data communication system for monitoring of NC machine fault diagnosis and status is developed. This system communicates with host PC by using dial-up communication method on PSTN. The developed system consists of (1)remote communication module among NC's and host PC using PSTN, (2) 8 channels analog data sensing module, (3) digital I/O module for control of NC machine, (4) communication module between NC machine and remote data communication system using RS-232c, and (5) Software man-machine interface. This system may be applied for remote sensing of the status in Fms. To show the veridity of the developed system, several examples are illustrated.