• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.57-63
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• 2022

This study describes a system that monitors the tool and cutting state of automatic beveling operation in real time. As a signal for cutting state monitoring, a motor current detected from the spindle drive system of the automatic beveling machine is used to monitor abnormal state. Because automatic beveling is processed using a face milling cutter, the cutting force mechanism is the same as the milling process. The predicted cutting torque is obtained using a cutting force model based on specific cutting resistance. Then, the predicted cutting torque is converted into the spindle motor current value, and cutting state stability is diagnosed by comparing it with the motor current value detected during beveling operation. The experimental results show that the spindle motor current can detect abnormal cutting state such as overload and tool wear during beveling operation, and can diagnose the cutting stability using the proposed equip-current line diagram.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.2
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• pp.47-57
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• 1990

In-process recognition of the cutting states is one of the very important technologies to increase the reliability of mordern machining process. In this study, practical methods which use the dynamic component of the cutting force are proposed to recognize cutting states (i.e. chip formation, tool wear, surface roughness) in turning process. The signal processing method developed in this study is efficient to measure the maximum amplitude of the dynamic component of cutting force which is closely related to the chip breaking (cut-off frequency : 80-500 Hz) and the approximately natural frequency of cutting tool (5, 000-8, 000 Hz). It can be clarified that the monitoring of the maximum apmlitude in the dynamic component of the cutting force enables the state of chip formation which chips can be easily hancled and the inferiority state of the machined surface to be recognized. The microcomputer in-process tool wear monitor- ing system introduced in this paper can detect the determination of the time to change cutting tool.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.3
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• pp.75-81
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• 1997

Automatic monitoring the cutting state is one of the important problems to increase the reliability of modern machining processes. In this study, cutting force signals were used in order to monitor the tool wear and fracture in the turning process. Turning experiments were performed using cemented carbide insert tools(K20) and STS304 steel as a workpiece. Cutting force signal characteristics and histogram analysis method were used to recognize the cutting states. It was found that tool wear and fracture can be diagnosed from the cutting force signal coefficient of variation(C.V.) and histogram analysis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.1-7
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• 2003

In order to predict vibrations occurred during end-milling processes, the cutting dynamics was modelled by using neural network and combined with structural dynamics by considering dynamic cutting state. Specific cutting force constants of the cutting dynamics model were obtained by averaging cutting forces. Tool diameter, cutting speed, fled, axial and radial depth of cut were considered as machining factors in neural network model of cutting dynamics. Cutting farces by test and by neural network simulation were compared and the vibration displacement during end-milling was simulated.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.658-659
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• 2006

Magnetron sputtered TiN, (Ti, Al)N and TiN/(Ti, Al)N multilayer coatings grown on cemented carbide substrates have been characterized by using electron probe microanalysis (EPMA), X-ray diffraction (XRD), scanning electron spectroscopy (SEM), nanoindentation, scratcher and cutting tests. Results show that TiN coating is bell mouth columnar structures, (Ti, Al)N coating is straight columnar structures and the modulation structure has been formed in the TiN/(Ti, Al)N multilayer coating. TiN/(Ti, Al)N multilayer coating exhibited higher hardness, better adhesion with substrate and excellent cutting performance compared with TiN and (Ti, Al)N coating.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.10
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• pp.135-140
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• 1997

The detection of cutting tool states in machining is important for the automation. The information of cutting tool states in metal cutting process is uncertain. Hence a industry needs the system which can detect the cutting tool states in real time and control the feed motion. Cutting signal features must be sifted before the classification. In this paper the Fisher's linear discriminant function was applied to the pattern recognition of the cutting tool states successfully. Cutting conditions and cutting force para- meters have shown to be sensitive to tool states, so these cutting conditions and cutting force paramenters can be used as features for tool state detection.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.653-657
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• 2000

Machining performance in hard turning of hardened AISI M2 steel has been studied. Ceramic tools were used in the cutting tests without coolants and workpiece was prepared by heat treatment to increase its hardness up to Rc 60. Cutting state parameters such as cutting forces, temperature, and tool wear were measured in the experiments and effects of tool wear on cutting states were investigated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.3
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• pp.111-117
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• 1997

The sudden-stop apparatus is made to measure the residual stress of the infinitesimal area at the turning work surface by using the X-ray stress apparatus. This study is trued to make the cutting work the instantaneous stopping state in the normal working state. The behaviour of work material near the tool is estimated. The estimation method is that the distribution of residual stress can be also measured. The object is to clarify and control the mechanism to leave the adequate stress of the finishing surface. It's beginning is due to observe the occurrence state of the residual stress at the cutting work. The result obtained by this study is as follows. The chips are not separated from the work materials at all the cutting experiments of built-up edges or the shearing areas etc. which can be precisely observed by using the sudden-stop apparatus. The strain of movable system which can be seen at the part of working layer means the size of strain. This experiment proves that the working strain should be lessened to make the size of strain control the residual stress happened at the cutting surface.