• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.445-451
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• 2016

The Inconel 718 alloy is a well-known super-heat-resistant alloy and a difficult-to-cut material. Inconel 718 with excellent corrosion and heat resistance is used in harsh environments. However, the heat generated is not released owing to excellent physical properties, making processes (e.g., adhesion and thermal fatigue) difficult. Tool condition monitoring in machining is significant in reducing manufacturing costs. The cutting tool is easily broken and worn because of the material properties of Inconel 718. Therefore, tool management is required to improve tool life and machinability. This study proposes a method of predicting the tool wear with non-contacting sensors (e.g., IR thermometer for measuring the cutting temperature and a microphone for measuring the sound pressure level in machining). The cutting temperature and sound pressure fluctuation according to the tool condition and cutting force are analyzed using experimental data. This experiment verifies the effectiveness of the non-contact measurement signals in tool condition monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.3
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• pp.75-82
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• 1990

Automatic monitoring of the cutting conditions is one of the most improtant technologies in machining. In this study, the feasibility in applying acoustic emission(AE) signals for the in-process detection of endmill wear and fracture has been investigated by performing experimental test on the NC vertical milling machine with SM45C for specimen. As the results of detecting and analyzing AE signals on various cutting conditions, the followings have confirmed. (1) The RMS value of acoustic emission is related sensitively to the cutting velocity, but is not affected largely by feed rate. (2) The burst type AE signals of high level have been observed when removing chips distorderly and discontinuously. (3) When the RMS value grows up rapidly due to the increase of wear the endmill are generally broken or fractured, but when the endmills fracture at the conditions of smooth chip-flow or built-up-edge(BUE) occurred frequently, the rapid change of the RMS arenot found. And it is expected that this technigue will be quite useful for in-process sensing of tool wear and fracture.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.478-490
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• 2022

TBM disc cutter, which is the main cutting tool of tunnel boring machine (TBM), is replaced when it is excessively worn during the boring process. Disc cutters are usually monitored by workers at cutterhead chamber, and they check the status and wear amount of cutters. Because cutterhead chamber is usually in dangerous circumstance due to high pressure and instability of excavation surface, the measurement by manpower occasionally results in inaccuracy of measurement result. In order to overcome the limitations, the real-time disc cutter monitoring techniques have been developed in some foreign countries. This paper collected the current status of disc cutter monitoring system from the literature. Several types of sensors are used to measure the cutter wear, and it is believed that the collected information can be useful reference when similar domestic technologies are developed in the future.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.1
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• pp.74-83
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• 1991

A study on the Correlation of Orthogonal Cutting Parameter (Cutting speed, Depth of cut, Feed Rate) and Acoustic Emission Signal. It is well known that acoustic emission (AE) is the emission of elastic wave resulting from the deformation and fracture of materials. This study estabished correlation of orthogonal cutting parameter and AE signal, and researched into in-process monitoring of tool wear and failure. The results are as follow; 1. AE RMS was under the influence of cutting speed but hardly influenced by depth of cut and feed rate. 2. AE RMS was under the influence of flank wear. 3. AE count rate increased by increased cutting speed. 4. AE RMS value was rapidly increased in 130 m flank wear.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.14-19
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• 2000

A machine tool has some serous stability problem in the from of tool chatter during the cutting process. Chatter vibration deteriorates the surface finish, reduce tool and machine life, accelerate machine tool system component wear, and may lead to an unacceptable noise sound in the working environment. In this study, in order to moni색 of the chatter vibration on the cutting process, the behavior of spectral density of AE signal and principal cutting force signal has been investigated. Furthermore, its reliability from obtained the results has been studied to evaluate and confirm the proposed method with the application procedure and the experimental results.

• 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.

• 한국기계연구소 소보
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• s.19
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• pp.61-68
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• 1989

As the system monitoring technology become required in order to improve the system performance and the productivity, we’ve studied to the detection for the tool wear and the tool breakage using AE sensors that is able to detection of generated high frequency stress pulse at cutting. The detection system is consist of a sensing part, a amplifier part, a signal processing part, and a analysis & output part. The moment (a rms and a kurtosis) of statistical method is used for analysis of AE singnal. The experiment are carried out in a CNC lathe. In this study, we achieved that the amplitude level of the AE signal and statistical moments was largely changed as the tool failure. The change rate of Kurtosis was especially large, but the change rate of the rms was small.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.89-96
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• 2021

The number of confirmed cases of coronavirus-19 is soaring around the world and has caused numerous deaths. Wearing a mask is very important to prevent infection. Incidents and accidents have occurred due to the recommendation to wear a mask in public places such as buses and subways, and it has emerged as a serious social problem. To solve this problem, this paper proposes an open source-based face mask wearing monitoring system. We used open source software, web-based artificial intelligence tool teachable machine and open source hardware Arduino. It judges whether the mask is worn, and performs commands such as guidance messages and alarms. The learning parameters of the teachable machine were learned with the optimal values of 50 learning times, 32 batch sizes, and 0.001 learning rate, resulting in an accuracy of 1 and a learning error of 0.003. We designed and implemented a mask wearing monitoring system that can perform commands such as guidance messages and alarms by determining whether to wear a mask using a web-based artificial intelligence tool teachable machine and Arduino to prove its validity.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.3
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• pp.470-476
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• 1997

The dynamic characteristics of industrial processes frequently cause an abnormal situation which is undesirable in terms of the productivity and the safety of workers. The goal of fault-tolerance is to continue performing certain activities even after the failure of some system cononents. A fault-tolerant intelligent monitoring and control system which is robust under disturbances is proposed in this paper. Specifically, the fault-tolerant monitoring scheme proposed consists of two process models and the inference module to preserve such a robustness. The results of turning experiments demonstrate the effectiveness of the fault-tolerant scheme in the presence of built-up edge.

• International Journal of Precision Engineering and Manufacturing-Green Technology
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• v.5 no.5
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• pp.571-581
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• 2018

In the present study, monitoring of elliptical vibration cutting process by utilizing internal data in the ultrasonic elliptical vibration device without external sensors such as a dynamometer and displacement sensor is investigated. The internal data utilized here is the change of excitation frequency, i.e. resonant frequency of the device, voltages applied to the piezoelectric actuators composing the device, and electric currents flowing through the actuators. These internal data change automatically in the elliptical vibration control system in order to keep a constant elliptical vibration against the change of the cutting process. Correlativity between the process and the internal data is described by using a vibration model of ultrasonic elliptical vibration cutting and verified by several experiments, i.e. planing and mirror surface finishing of hardened die steel carried out with single crystalline diamond tools. As a result, it is proved that it is possible to estimate the elements of elliptical vibration cutting process, e.g. tool wear and machining load, which are important for stable cutting in such precision machining.