• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1060-1061
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• 2015

국내 공작기계 시장은 일본 화낙사 컨트롤러를 사용하는 비중이 80[%] 전후이며 그 비중이 높다고 할 수 있다. 최근, 기계가공 분야에서도 IoT(Internet of Thins) 환경을 도입하여 공장의 스마트화가 이루어지고 있다. 대부분의 공작기계를 소유하고 있는 산업현장에선 공작기계에 관한 모니터링 시스템에 대한 필요성을 인식하고 있으며, 공작기계의 가동률 체크 뿐만 아니라 가공패턴 및 가공시간 등을 견적서 자료로 활용하려하고 하고 있다. 이러한 요구조건을 충족시키기 위하여 가공기계 분야에도 ERP(Enterprise Resource Planning)시스템을 도입하여 생산 공정관리의 기초 데이터로 활용하고 있다. 이러한 생산 현장을 감안하여 공작기계에 대한 공정 모니터링 기능 및 스마트화가 무엇보 다 중요하다고 할 수 있다. 본 논문에서는 화낙(fanuc)에서 제공하는 FOCAS 라이브러리를 활용하여 공작기계 가동률에 대한 체크가 가능하도록 Wi-Fi 기반의 데이터 전송 모듈을 개발하여 공작기계 모니터링에 필요한 소형의 통신모듈을 제시하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.539-540
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• 2015

뿌리산업 중 모든 산업을 뒷받침하고 있는 금형과 소성가공 분야에 대한 핵심분야는 공작기계(NC/CNC)에 있다고 볼 수 있다. 금형과 가공기계 분야 중 금형분야에 적용되는 대부분의 공작기계는 대부분 i-계열(모니터링 기능탑재)이 대부분이며, 지속적 성장세를 보이고 있다. 이러한 추세를 보아 향 후 10년 이내에 가공분야 적용되는 공작기계는 모니터링 기능을 기본적으로 탑제된 시스템이 널리 보급 될 것으로 전망된다. 이러한 산업의 변화에 맞추어 모니터링 프로그램의 활용과 다양한 정보를 요구하고 있지만, 실제 금형산업 현장의 대부분은 모니터링 프로그램을 활용 할 수 없는 오래된 구형장비들로 이루어져 있어 새로운 기능 제품에 대한 도입이 필요한 실정이다. 본 논문에서는 이러한 구형 모델에 대한 단점을 보완하기 위하여 기존의 구형장비를 신형장비와 같이 공작기계의 장업상태를 모니터링 할 수 있는 장비를 연구개발하여 실용화 가능성을 검토하고자 한다.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1175-1182
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• 2017

Recent trend in the manufacturing industry is focused on the convergence with IoT and Big Data, by emergence of the 4th Industrial Revolution. To realize a smart factory, the proposed system based on MTConnect technology collects and integrates various status information of machines from many production facilities including heterogeneous devices. Also it can distribute the acquisited status of heterogeneous manufacturing machines to the remote devices. As a key technology of a flexible automated production line, the proposed system can provide much possibility to manage important information such as error detection and processing state management in the unmanned automation line.

• Proceedings of the KAIS Fall Conference
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• 2001.11a
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• pp.95-96
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• 2001

본 논문에서는 스마트재료 및 구조물(smart materials and structures : SMS)들 중 잠재적 응용가치가 가장 큰 것으로 인식되는 전기유동유체(electro-rhelogical fluid 이하 ER유체)의 이론적 근거 및 역학적 거동과 잠재적 응용성에 관한 연구이다. 이를 적용하기 위하여 공작기계의 브레이크에 적용하여 모델링 및 해석, 제어알고리즘 설계하여 정밀도에 양호한 결과를 얻었다.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.1
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• pp.9-16
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• 2015

The machining-chatter stands for a sudden relative vibration appeared between a material and a tool while processing with a machine. This chatter is key factor that seriously affects the quality of processed materials as well as being a factor which causes serious damages to the tool and the machine. This study is related to the monitoring and smart control of chatter problem that can compensate machining-chatter faster and produce processed goods with more precision by autonomous compensation. The above-mentioned machining-chatter compensator includes the chatter vibration sensor and the chatter compensator that estimates the compensation value according to the sensor detecting the chatter vibration of machine-tool and the chatter vibration detected from the sensor while having a feature of being organized by interlocking with the machine-tool controller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.338-343
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• 2018

Recently, a computer numerical control (CNC) machine is used widely for mold making in various industries. In the operation of a CNC machine, the production quality and safety of workers are becoming increasingly important as the product process increases. A variety of tool life prediction studies has been conducted to standardize the quality of production and improve reproducibility. When the tool life is predicted using the conventional tool life equation, there is a large error between the experimental result and result by the conventional tool life equation. In this paper, an algorithm that can predict the precise tool life was implemented using a genetic algorithm.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.165-166
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• 2020

Internationally, manufacturers attempted respectable portion of in-house logistics to satisfy end users and decrease manpower to compete for manufacturing price and quality optimization. Mostly, manufacturers operate variety of facilities such as collaborative robots, conveyor, etc. based on PLC. To achieve it, manufactures shall operate the optimized number of manufacturing processes with logic controlled by computer to reduce human errors. In prior to it, manufacturing industry still own plenty of fields which have not yet been adjusted with automation. For example, we shall put in-house logistics on the issue. This study focuses on manufacturing industry, evaluate efficiency, costs, etc. in all aspects and suggest alternatives by analysis SWAT and OEE, let alone reason of weakness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.651-653
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• 2015

Common Embedded boards like the Arduino, Raspberry Pi, BeagleBone Black, leverages smart home systems, machine tools and various products in our day to day life. Managing and dealing frequent large scale incidents involving recent reagents and hazardous materials can be dangerous and difficult to detect in advance like in an event of an accidents or fires. In this paper I have done research by utilizing an Embedded (BeagleBone Black) boards sensors and RFID management system to detect a hazardous situation like fire in real time and avoiding it by sending out an alert message to the admin user to minimizing the risk. This system provides immediate information to the administrator of any hazardous situation and prevents any accidents from occurring.