• Structural Engineering and Mechanics
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• 제76권2호
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• pp.175-191
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• 2020

In this study, the effect of Nano silica (SiO2) on the buckling strength of the glass fiber reinforced laminates containing the machining process causes holes were investigated. The tests have been applied on two status milled and non-milled. To promote the mechanical behavior of the fiber-reinforced glass epoxy-based composites, Nano sio2 was added to the matrix to improve and gradation. Nano sio2 is chosen because of flexibility and high mechanical features; the effect of Nanoparticles on surface serenity has been studied. Thus the effect of Nanoparticles on crack growth and machining process and delamination caused by machining has been studied. We can also imply that many machining factors are essential: feed rate, thrust force, and spindle speed. Also, feed rate and spindle speed were studied in constant values, that the thrust forces were studied as the main factor caused residual stress. Moreover, entrance forces were measured by local calibrated load cells on machining devices. The results showed that the buckling load of milled laminates had been increased by about 50% with adding 2 wt% of silica in comparison with the neat damaged laminates while adding more contents caused adverse effects. Also, with a comparison of two milling tools, the cylindrical radius-end tool had less destructive effects on specimens.

• 융합신호처리학회논문지
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• 제5권4호
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• pp.281-286
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• 2004

FMS, FMC, FA, IMS의 구축에 있어서 최하위 단위인 공작기계의 자동화가 중요하다. 이를 위해서는 공작기계의 공구 감시기능(tool monitoring system)이 수행되어야 한다. 본 논문은 공구 감시기능의 자동화를 위해 종전의 공구마모 검출방법과는 달리 엔드밀의 마모상태에 따라 발생하는 절삭음의 음향주파수 분석을 통해 마모정도를 검출하는 방법을 제안하였다. 즉, 머시닝센터에서 공구마모가 잘되는 합금공구강재를 사용하고 이때 발생하게 되는 절삭음(cutting sound)을 음향 분석하여 공구 마모와 관련이 있는 가진 주파수(tooth passing frequency)를 찾아내고 또한 이 주파수의 크기 값과 공구마모(flank wear) 변화를 연구하여 엔드밀의 마모 상태를 추정하였다 이를 위해 본 연구에서는 실험 장비를 구성하고 절삭속도, 엔드밀마모, 공구직경을 절삭조건으로 하여 측정된 절삭음을 FFT 처리하였다. 또한 측정된 값을 회귀분석으로 모델링한 결과 엔드밀 마모 검출오차범위가 5.8% 이내로 나타나 음향주파수 분석에 의한 엔드밀 마모검출 방법의 유효성을 확인할 수 있었다.

• 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.