• Structural Engineering and Mechanics
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• v.76 no.2
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.281-286
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• 2004

The wear process of end mill is a so complicated process that a more reliable technique is required for the monitoring and controling the tool life and its performance. This research presents a new tool wear monitoring method based on the sound signal generated on the machining. The experiment carried out continuous-side-milling for using the high-speed steel end mill under wet condition. The sound pressure was measured at 0.5m from the cutting zone by a dynamic microphone, and was analyzed at frequency domain. The tooth passing frequency appears as a harmonics form, and end mill wear is related with the first harmonic. It can be concluded from the result that the tool wear is correlate with the intensity of the measured sound at tooth passing frequency estimation of end mill wear using sound is possible through frequency analysis at tooth passing frequency under the given circumstances.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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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.