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맹민재,Maeng, Min-Jae 한국기계가공학회 2004 한국기계가공학회지 Vol.3 No.1
An on-line monitoring system of endmill failure such as weal, chipping, and fracture is developed using AE, cutting force Characteristic variations of AE and cutting force signals due to endmill failure are identified as follows. When endmill fracture occurs, AE count rate shows a rapid Increase in conjunction with a subsequent decrease while a standard deviation of the principal cutting force Increases significantly. The increase of AE count rate precedes the Increase of standard deviation of principal cutting force. Chipping results in relatively small increase and decrease of AE count rate without any significant variation of the cutting force Gradual increase of AE count rate and mean principal cutting force are Identified to be related with the wear of cutter. A cutter fracture detection algorithm is developed based on the present results. The signals me normalized to enhance the applicability of the algorithm to Wide those of fresh cutters, and qualitative characteristics of AE signals encountered at the moment of fracture are employed. It is demonstrated that the algorithm can detect the cutter fracture successfully.
맹민재 한국안전학회 2001 한국안전학회지 Vol.16 No.5
Wire electrical discharge machining experiments in conducted to investigate characteristics of acoustic emission (AE) and electrical discharge energy due to current peak (I$_{p}$), pulse on time($\tau$/on/). The AE signals are obtained with a sensor attached to workpiece side. Machining states are identified with scanning electron microscopy and residual stress analyzer. It is demonstrated that the residual stress provide reliable informations about the machining states. Moreover, machining states can be detected successfully using both the residual stress and AE count rate.e.
정준기,맹민재 弘益大學校 科學技術硏究所 1997 科學技術硏究論文集 Vol.8 No.-
In order to achieve the automation and untended system of manufacturing process, it is necessary that the monitoring system check up the disorder of machine tool. In this study, the method which uses acoustic emission(AE) signal is proposed to monitor the tool fracture in the milling process. In order to detect the AE-signal from the cutting zone directly, the AE-sensor is installed in the cutting oil supply device. Due to the tool fracture, the amplitude level of AE-signal at the beginning of tool-workpiece contact increases with the progressing cutting time. Using this phenomena, a practical monitoring algorithm has been developed. The practical method was also proposed to detect the tool fracture.
선삭가공에서 공구마멸에 따른 절삭력과 AE 신호의 특성 연구
맹민재,정준기 한국공작기계학회 1995 한국생산제조학회지 Vol.4 No.2
In order to achieve the automation and untended system of manufacturing process, it is necessary that the monitoring system check up the disorder of machine tool or the conditions of tool wear for the maximum use of cutting tool. In the metal cutting process, AE signal is detected by AE sensor, then amplified and transmitted to an Locan-AT. The experiment was performed to SM25C and STS304 steels at uniform feedrate, cutting speed and depth of cut. The results of experimental data apparently showed emission intensity vary due to increasing of tool wear at the 165kHz, 200kHz in SM25C and 140kHz, 165kHz, 200kHz in the STS304 respectively. Therefore, it is possible to predict the tool wear. This study is intended to suggest the way to the automation and untended system of machine tool through the system monitoring tool wear by using AE signal.
맹민재,정준기 韓國工作機械學會 2000 한국생산제조학회지 Vol.9 No.4
Even in a fully automated factory, many deburring operations are carried out manually. To remove or minimize the burr effectively or automatically, understanding of the burr formation which occur at the exit stage of machining is necessary. Burrs can be formed on the feed mark ridges and the edges of the machined parts in machining operations. These burrs are undesirable in terms of the surface quality, the precise dimensioning of the machined parts and the safety of operators. This paper demonstrates the effectiveness of using end mill tool on minimizing the exit burr formation in machining. In particular, the experimental relationships between the size of exit bun and the cutting parameters are established in end mill machining. Methods to control the size of exit burr are then explained.
鄭準基,孟玟在 弘益大學校 科學技術硏究所 1994 科學技術硏究論文集 Vol.4 No.-
Acoustic Emission signal can detect the state of tool wear and fracture in turning. In metal cutting process. AE signal is detected by AE sensor, then amplified and transmitted to analysing equipment. Experiments were performed in SM25C and STS304 steels at uniform feed-rate, cutting speed and depth of cut. The results of experimental data show apparently that the emission intensity is generated due to the growth of tool wear at the 165KHz, 200KHz in the SM25C and 140KHz, 165KHz, 200KHz in the STS304 respectively.