• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.11
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• pp.1106-1112
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• 2012

This paper deal with a technique for monitoring machining conditions by measuring the vibration of cutting forces at milling machining. The vibrations of cutting forces in milling process were measured and analyzed to be related with processing parameters. The magnitude of cutting force is linearly proportional to the feed rate and cutting depth, and frequency of cutting force is linearly proportional to the rotating speed. Wired and wireless communication methods were applied in transmitting the measured vibration signals and the two methods were compared. The magnitude of the vibration signals transmitted by the wireless communication method was similar to that transmitted by the wired communication method.

• 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.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.185-193
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• 1997

This paper presents the on-line monitoring of the surface roughness in a face milling operation. The cut- ting force was used to monitor the surface roughness, since the insert run-outs not only deteriorate surface roughness but also change cutting force. AR model and band energy method were taken to extract the fea- tures from the cutting force. The features extracted from AR modelling are more accurate about the moni- toring than those from band energy method, whereas, the computing speed of the former is slow. An artifi- cal neural network discriminated the level of the surface roughness by using the features extracted via signal processing.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.1
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• pp.1-9
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• 2001

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining system A major topic relevant to metal-cutting operations is monitoring toll wear, which affects process efficiency and product quality, and implementing automatic toll replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. The static com-ponents of cutting force have been used to detect flank wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the force modeling is performed for various cutting conditions. The normalized force dis-parities are defined in this paper, and the relationships between normalized disparity and flank were are established. Final-ly, artificial neural network is used to learn these relationships and detect tool wear. According to proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.178-185
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• 1999

In tool condition monitoring systems, parameters should be set to a certain threshold. In many cases, however, the threshold is dependent on cutting conditions, especially the radial immersion ratio. In this presented is a method of on-line estimation of the radial immersion ratio in face milling. When a tooth finishes sweeping, a sudden drop of cutting force occurs. The force drop is equal to the cutting force that acting on a tooth at the swept angle of cut and can be acquired from cutting force signals in feed and cross-feed directions. Average cutting force per tooth period can also be calculated from cutting force signals in two directions. The ratio to cutting forces in two directions acting on a tooth at a certain swept angle of cut and the ratio of average cutting forces in two directions per tooth period are functions of the swept angle of cut and the ratio of radial to tangential cutting forces. Using these parameters, the radial immersion ratio is estimated. Various experiments are performed to verify the proposed method. The results show that the radial immersion ratio can be estimated by this method regardless of other cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.61-66
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• 1994

Tool condition monitoring is one of the most important aspects to improve productivity and quality and to achieve intelligent machining system. The tool state is classified into three groups as chipping, wear and fracture. In this study, wear of a ceramic cutting tool for hardened die material (SKD11) was investigated. Flank wear was occured more dominant than crarer wear. Therefore, to predict flank wear, the modeling of cutting force has been performed. The modeling of cutting force by an assumption that act the stress distribution on the tool face obtained through a numerical analysis. The relationships between the cutting force and the tool wear can be constructed by machining paraneters with cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions of the flank wear is approximately in good agreement with experimental result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.84-87
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• 2001

The high speed machining which cam improve the production and quality has been remarkable in die/mold industry with the growth of parts and materials industries. The speed of machine tool increases, but on the other hand, the response of sensors I not being improved. Therefore, the condition monitoring techniques for the machine too, tool and workpiece in high speed machining are incomplete. In this study, characteristics of the tool edge roughness were verified from the high frequency components of cutting force signals acquired by the high speed dynamometer. Also, the experimental evaluation technique for the machinability and condition monitoring in high speed machining was established by analyzing the cutting force, acceleration and surface roughness.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.189-198
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• 1998

Recently, monitoring of tool life is a matter of common interesting because tool life affects precision, productivity and cost in machining process. Especially flank wear has a direct effect on cutting mechanism, so the various pattern of cutting force is obtained experimentally according to variation of wear condition. By investigating cutting force signal, AR(Autoregressive) modeling and correlation dimension analysis is conducted in turning operation. In this modeling and analysis, we extract features through 6th AR model, correlation integral and normalized correlation integral. After the back-propagation model of the neural network is utilized to monitor tool life according to flank wear. As a result. a very reliable classification of tool life was obtained.