• 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.14 no.12
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• pp.48-55
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• 1997

A useful method to detect tool breakage suing neural network of cutting force signal is porposed and implemented in a basic cutting process. Cutting signal is gathered by tool dynamometer and normalized as a preprocessing. The cutting force signal level is continually monitored and compared with the predefined level. The neural network has been trained normalized sample data of the normal operation and cata-strophic tool failure using backpropagation learning process. The develop[ed system is verified to be very effective in real-time usage with minor modification in conventional cutting processes.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.25-31
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• 1996

In this paper, three different types of commercial tools -P20, NC123K and ceramic- have been used to cut austempered ductile iron(ADI). In the austempered condition the materials are hard, strong and difficult to machine. Thus, we selected a optimum tool material among three different types of used tools in machining of austempered ductile iron. It was used acoustic emission (AE) to know cutting characteristic for selected tool and investigate characteristic of AE signal according to cutting condition and relationship between AE signal and flank wear land of the ceramic tool. The obtained results are as follows ; (1) The ceramic tool among three different types of tools is the best in machining austempered ductile iron. (2) In case of ceramic tool, the amplitude level of AE signal(AErms) is mainly affected by cutting condition and it is proportional to cutting speed. (3)There have been the relationship of direct proportion between the amplitude level of AE signal and flank wear land of the tool. (4) It was observed that the value of AErms was only affected by cutting speed. Therefore it is possible to in-process detec- tion of ceraic tool's wear in case the initial value of AErms at each cutting speed decided.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.526-530
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• 1996

In this paper, three different types of commercially tools-P20, NC123K and ceramic-have been used to working austempered ductile iron(ADI). In the austempered condition the materials are hard, strong and difficult to machine. Thus, we selected a optimum tool material among three different types of used tools in machining of austempered ductile iron. It was used acoustic emission(AE) to know cutting characteristic for selected tool and flank wear land of the ceramic too. The obtained results are as follows; (1) The ceramic tool among three different types of tools is the best in machining austempered ductile iron. (2) In case of ceramic tool, the amplitude level of AE signal(AErms) is mainly affected bycutting speed in cutting speed in cutting condition and it is proportioned to cutting speed. (3) There have the relationship of direct proportion between the amplitude level of AE signal and flank wear land of the tool. (4) If it find the value of AErms at each cutting speed, the in-process detection to ceramic tool's wear is possible

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.150-159
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• 2000

In-process diagnosis of the cutting state is essential for the automation of manufacturing systems. Especially when the cutting process becomes unstable it induces self-exited vibrations a frequent case of poor tool life rough surface finish damage to the workpiece and the machine tool itself and excessive down time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time monitoring and controlling chatter. This paper describes the detection method of chatter vibration using cutting force in turning process. In order to detect a chatter vibra-tion the dynamic fluctuation of radial force is analyzed since this components is sensitive to the chatter. The envelope sig-nal of radial force has been calculated by the use of FIR Hilbert transformer and it was useful to classify the chatter signal from the dynamically unstable circumstances. It was found that the mode and the mode width were closely correlated with the chatter amplitude was well. Finally back propagation(BP) neural network have been applied to the pattern recognition for the classification of chatter signal in various cutting conditions. The validity of this systed was confirmed by the experiments under the various cutting conditions.