• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.70-75
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• 1993

Chatter vibration is an unwanted phenomenon in metal cutting and it always affects surface finish, tool life machine life and the productivity of machining process. The In-process monitoring & control of chatter vibration is necessarily required to automation system. In this study, we constructed the multi-sensing system using Tool Dynamometer,Accelerometer and AE(Acoustic Emission) sensor for the credible detection of chatter vibration. And a new approach using a neural network to process the features of multi-sensor for the recognition of chatter vibration in turning operation is proposed. With the back propagation training process, the neural network memorize and classify the feature difference of multi-sensor signals.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.817-821
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• 1997

Ever since the nonlinearity of machine tool dynamics was established, researchers attempted to make use of this fact to devise better monitoring, diagnostics and system, which were hitherto based on linear models. Theory of chaos, which explains many nonlinear phenomena comes handy for furthering the analysis using nonlinear model. In this study, measuring system will be constructed using multi-sensor (Tool Dynamometer, Acoustic Emission) in end millingprocess. Then, it will be verified that cutting force is low-dimensional deterministic chaos calculating Lyapunov exponents, Fractal dimension, Embedding dimension. Aen it will be investigated that the relations between characteristic parameter caculated form sensor signal and tool wear.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.2
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• pp.18-24
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• 1995

In order to achieve the autimation 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 the 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.1
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• pp.1-7
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• 2007

As fiber reinforced composite materials are widely used in aircraft, space structures and robot arms, the study on non-destructive testing methods has become an important research area for improving their reliability and safety. AE (acoustic emission) can evaluate the defects by detecting the emitting strain energy when elastic waves are generated by the initiation and growth of crack, plastic deformation, fiber breakage, matrix cleavage, or delamination. In the paper, AE signals generated under uniaxial tension were measured and analyzed using the $8{\times}8$ matrix piezo electric sensor. The electronic circuit to control the transmitting distance of AE signals was designed and constructed. The optical data storage system was also designed to store the AE signal of 64channels using LED (light emitting diode) elements. From the tests, it was shown that the source location and propagation path of AE signals in composite materials could be detected effectively by the $8{\times}8$ matrix piezo electric sensor.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.817-823
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• 2020

In recent, the machining of difficult-to-cut materials such as titanium alloys, stainless steel, Inconel, ceramic, glass, and carbon fiber reinforced plastics (CFRP) used in aerospace, automobile, medical industry is actively researched. Abrasive waterjet is a non-traditional processing method in which ultra-high pressure water and abrasive particles are mixed in a mixing chamber and shoot out jet through a nozzle, and removed by erosion due to collision with a material. In particular, the nozzle of the abrasive waterjet is one of the most important parts that affect the machining quality as with a cutting tool in general machining. It is very important to monitor the condition of the nozzle because the workpiece is uncut or the surface quality deteriorates due to wear, expanding of the bore, damage of the nozzle and clogging of the abrasive, etc. Therefore, in this paper, we propose a monitoring system based on Acoustic Emission(AE) sensor that can detect nozzle condition in real time during AWJ processing.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.394-400
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• 2012

Due to the increasing demand for carious methodologies, the quality improvement of products were introduced. A brush, the most frequently used type of grinding process, is one of the deburring. In order to produce consistent burr shape, various machining conditions have been combined and applied to disk grinding process. By tool dynamometer, acoustic emission sensor and acceleration sensor depend on changes in processing conditions(depth of engagement, cutting speed, workpiece position, workpiece orientation, cutting time) signals were obtained for brush deburring grinding system. Root mean square obtained by processing the signal processing conditions by analyzing the characteristics of deburring is to derive the optimum conditions.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.8
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• pp.466-469
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• 2016

In this study, in order to develop composition ceramics for Acoustic Emission (abbreviated as AE) sensor application, the PZT system ceramics was fabricated by conventional solid state reaction method. When x=0.48, the density, electromechanical coupling factor($k_p$), piezoelectric coefficient $d_{33}$ and piezoelectric voltage constant $g_{33}$ of the maximum values of $7.857g/cm^3$, 0.51, 190[pC/N], 52[$10^{-3}mV/N$] were obtained, respectively, suitable for AE sensor.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.560-564
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• 1999

Burr sensing for burr size measurement and deburring process control is one of the essential elements in an automated deburring procedure. This paper presents the implementation of capacitance sensing and acoustic emission (AE) to deburring. The first application is the "on-line" measurement of burrs using a capacitance sensor. A non-contact capacitance gauging sensor is attached to an ultra precision milling machine which was used as a positioning system. The setup is used to measure burr profiles along machined workpiece edges. The proposed scheme is shown to be accurate, easy to setup, and with minor modifications, readily applicable to automatic deburring processes. As the second example, AE signals were sampled and analyzed for the sensor feedback of a precision deburring process - laser deburring -. The results, such as the sensitivity of AE signals to burr shapes and edge detection capability show a clear correlation between physical process parameters and the AE signals. A subsequent control strategy for deburring automation is also briefly discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.3
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• pp.155-161
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• 1996

For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program.