• 한국정밀공학회지
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• 제32권1호
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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.

• 한국기계가공학회지
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• 제20권9호
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• 한국BIM학회 논문집
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• 제8권3호
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• 한국안전학회지
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• 제37권4호
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• pp.1-10
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• 2022

The purposes of this study were to suggest 1) a future direction for Korea's machine tool industry and 2) how to secure the safety and reliability of emerging intelligent or automated machine tooling. The study concludes that, overseas, the machine tool industry is growing again while promoting innovation by converging with ICT. Accordingly, Korea also promotes ICT innovation to advance the machine tool industry, which is at the core of the national economy. As a result, unlike in the past, the frequency of serious injuries like entrapment accidents has recently decreased, while the proportion of collision accidents has increased. In addition, a new type of accident has become possible. Since ICT is network-based, the distinction between work and rest can become ambiguous; there is a risk of hacking, working hours and places are flexible and there are risk factors for diseases like chronic fatigue due to overload of specific personnel. As robots and automation are introduced, there is also a high probability of problems caused by physical and psychological burdens on system operators and resulting fatigue.

• 한국기계가공학회지
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• 제8권1호
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• pp.57-63
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• 2009

Magneto-Rheological(MR) fluid composes of a base fluid and ferromagnetic particles less than tens of micrometer size dispersed in the fluid. It is called as a smart material because its rheological properties are changable by a magnetic field. Its important applications are active devices such as controllable dampers and controllable clutches. The merit of those products is that their functional characteristics are controllable such that they enable active control strategies. This paper proposes an idea for machine tool applications of the MR fluid clutch as a safety device for power transmission. FEM has been used for magnetic field analyses and the results are compared with some former experiments. Some design syntheses of the MR clutches are suggested and hopefully considered that it may be an effective safety device for power transmission of machine tools.

• 융합신호처리학회논문지
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• 제23권2호
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• pp.84-90
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• 2022

공작기계 상태 진단은 기계의 상태를 자동으로 감지하는 프로세스이다. 실제로 가공의 효율과 제조공정에서 제품의 품질은 공구 상태에 영향을 받으며 마모 및 파손된 공구는 공정 성능에 보다 심각한 문제를 일으키고 제품의 품질 저하를 일으킬 수 있다. 따라서 적절한 시기에 공구가 교체될 수 있도록 공구 마모 진행 및 공정 중 파손 방지 시스템 개발이 필요하다. 본 논문에서는 공구의 적절한 교체 시기 등을 진단하기 위해 딥러닝 기반의 계층적 컨볼루션 신경망을 이용하여 5가지 공구 상태를 진단하는 방법을 제안한다. 기계가 공작물을 절삭할 때 발생하는 1차원 음향 신호를 주파수 기반의 전력스펙트럼밀도 2차원 영상으로 변환하여 컨볼루션 신경망의 입력으로 사용한다. 학습 모델은 계층적 3단계를 거쳐 5가지 공구 상태를 진단한다. 제안한 방법은 기존의 방법과 비교하여 높은 정확도를 보였고, 실시간 연동을 통해 다양한 공작기계를 모니터링할 수 있는 스마트팩토리 고장 진단 시스템에 활용할 수 있을 것이다.

• 한국기계가공학회지
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• 제16권5호
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• 한국공작기계학회논문집
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• 제10권4호
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• pp.111-117
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• 2001

In this paper, we will propose the new method that estimates the sensing ability of smart sensor. A study is estimation method that evaluates the sensing ability about smart sensor respectively. According to acceleration(g) and displacement changing, we estimated the sensing ability of smart sensor using the SAI(Sensing Ability Index) method respectively. We made the smart sensors in our experiment. The types of smart sensor are three types(H1, H1, H3 smart sensor). The smart sensors were developed for recognition of materials. Experiments and analysis were executed to estimated the sensing abili-ty of smarty sensor. Dynamic characteristics of smart sensors(acceleration changing) were evaluated respectively through a new method(SAI) that uses the power spectrum density.

• 산업경영시스템학회지
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• 제43권4호
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• 한국공작기계학회논문집
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• 제13권1호
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• pp.22-27
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• 2004

A new method that estimates the sensing ability of HH smart sensor is proposed. The new signal processing method have been developed that can distinguish among different materials relatively. The HH smart sensor was developed far recognition of materials. The HH smart sensor was made for experiment. Then, it was estimated the ability to recognize objects according to acceleration value. The sensing ability of HH smart sensor has been estimated with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to acceleration value changing. Dynamic characteristics of HH smart sensor were evaluated relatively through a new $R_{SAI}$ method that uses the power spectrum density. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.