• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.15-21
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• 2019

The fourth industrial revolution is noted. It is a smarter factory. At present, research on CNC (Computerized Numeric Controller) is actively underway in the manufacturing field. Domestic CNC equipment, acoustic sensors, vibration sensors, etc. This study can improve efficiency through CNC. Collect various data such as X-axis, Y-axis, Z-axis force, moving speed. Data exploration of the characteristics of the collected data. You can use your data as Random Forest (RF), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM). The result of this study is CNC equipment.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.729-730
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• 2009

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.210-212
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• 2022

In the manufacturing industry, various sensors are attached to monitor the status of production facility. In many cases, the data obtained through these sensors is time series data. In order to determine whether the status of the production facility is abnormal, the process of extracting patterns from time series data must be preceded. Also various methods for extracting patterns from time series data are studied. In this paper, we use matrix profile algorithm to extract patterns from the collected multivariate time series data. Through this, the pattern of multi sensor data currently being collected from the CNC machine is extracted.

• 기계와재료
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• v.22 no.1
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• pp.46-53
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• 2010

공작기계에서 가공정밀도를 저하시키는 가장 큰 요인은 열변형 및 채터진동이다. 본 고에서는 이 중 장시간 가공중 기계의 열변형에 따른 문제점을 자동으로 공작기계 CNC(Computerized Numerical Controller) 제어기상에서 실시간으로 보상하여 주는 장치 및 기술개발 사례에 대한 내용을 언급하고자 한다. 기계가공에서 온도신호의 실시간 데이터 취득 및 열변형에 따른 공작기계 원점(Work Offset)의 자율보정이 가공정밀도 향상 및 가동률 향상에 많은 영향을 끼친다 이에 따라 본 고에서는 온도 데이터의 취득부와 보상을 위한 보정값 추출을 위한 선형회귀법 및 신경회로망의 보정모델을 임베디드화한 디바이스와 CNC상에서 가공중 공작기계 원점 자동보정을 하는 시스템을 개발하였기에 관련내용을 소개하고자 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.181-188
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• 2024

With the development of precision facility technology, the importance of four-axis CNC precision processing technology is increasing. However, due to complex shape processing, it is difficult to operate the system. This paper compared precision control and efficiency by manufacturing a four-axis control structure to solve this problem and implementing Bresenham's Line Algorithm and Runspeed Algorithm without using commercial programs. When Runspeed was applied, there was an error improvement of about 12.03% and curvature accuracy improvement compared to Bresenham. It was confirmed that precise processing was possible to a three-dimensional structure by driving a rotary shaft A motor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.204-206
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• 2022

Digital twin, which is evaluated as the core of smart factory advancement, is a technology that implements a digital replica in the virtual world with the same properties and functions of assets in the real world. Since the smart factory to which digital twin is applied can support services such as real-time production process monitoring, production process simulation, and predictive maintenance of facilities, it is expected to contribute to reducing production costs and improving productivity. AAS (Asset Administration Shell) is an essential technology for implementing digital twin and supports a method to digitally represent physical assets in real world. In this paper, we design AAS for manufacturing data gathering to be used in real-time CNC (Computer Numerical Control) monitoring system in operation by considering manufacturing facility in smart factory as assets.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.16-23
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• 2014

Most of metal cutting and processing companies are going through the difficulties such as frequent downtime and high failure rates by missing times to replace parts of CNC equipment. The reason is that the correct operation times of expensive CNC equipment can't be calculated. In this paper, to solve this problem, we try to construct the intelligent production information system for a case company and analyze its expected effects. The effects are as follows. First, it saves money about 3 million won each year during the useful life of three years by reducing downtime and failure rates, and increasing production rates etc. Second, it makes easily find and fix the cause of faulty through traceability of the production process, and enables to perform a systematic facility management through the calculation of reliable equipment's overall efficiency. This case can be easily applied in similar industries, and will contribute greatly to enhancing the competitiveness of related industries.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.193-198
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• 1994

다축가공은 3축 이상의 동시제어축을 이용하여 복잡한 형상을 효율적으로 가공할 수 있는 첨예의 기술인 반면, 가공 설비의 고가로 인해 실제현장에 보급되지 못하고 있는 실정이다. 부가축 방식에 의한 저축화 가공방식은 이러한 현실적 문제에 대처할 수 있는 강력한 방식으로서, 본 연구팀에서는 3축 CNC 공작기계에 부가축 테이블 방식을 이용하여 5축 곡면가공을 구현한 바 있으며, 정삭가공 알고리즘을 개발한 바 있다. 본 연구에서는 부가축 환경하에서 황삭가공 알고리 즘을 다루며, 기존의 전축환경의 황삭가공에 비해공구자세를 인텍싱 형태로 변화시킬 수 있다는 차이가 있으며, 이에 따라 자세조정횟수의 초소화가 생산성 지표로 부각된다. 본 연구에서 개발된 황삭경로 알고리즘은 자세조정횟수를 포함 하여 공구접근영역, 공구교환횟수, 피드조정을 통하여 전체적을 황삭가공시간의 최소화로 접근하였다. 연구된 알고리즘 은 컴퓨터시뮬레이션을 통하여 검증하였으며, 실제절삭을 통한 검증이 추진중에 있다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.351-356
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• 1993

최근의 프로세스 공업화에 있어서 생산Line의 장치나 기계류는 점차 대형화, 고속화,연속화,복잡화되고 있다. 또한, 기계가공공업,자동차공업,기계,전자부품의 가공조립등의 생산설비는 각설비가 고도로 자동화되고 있는 실정으로 공장 전체의 유기체적인 제어 및 감독을 필요로 하고 있다. 마찬가지로 기계부품제작산업도 CNC.FMS등으로 점차 조작화,자동화됨에 따라 공작기계 장치나 기계류등의 이상이나 고장으로 생산 및 품질에 미치는 영향도 종래와 비교할 수 없을 정도로 중요시 되고 있는 실정이다, 이와같이 설비의 안전성을 도모하고 고신뢰도를 부여하기위해서는 기계설비의 이상 및 고장진단이 필수적이며, 공장 자동화와 함께 공작기계자체의 고장 및 이상진단을 실시하고, 검출된 신호의 크기등으로 고장상태를 판정해야만 한다. 공작기계에서 동적인 회전시스템을 이루는 주축용베어링의 손상은 제작하고자 하는 제품의 정밀도 표면거칠기등의 저하 뿐만아니라 시스템 전체의 기능까지도 떨어뜨리는 요인이 될수 있으므로 베어링 상태를 진단하여 송상유무를 판단하는것은 필수적이라 생각된다.