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

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.03a
• /
• pp.30-34
• /
• 1998

In-process monitoring of cutting conditions and tool wear is important for improving productivity. This paper is concerned with on-line monitoring of tool wear and cutting force in end milling operation. The experimental study deals with the relations between flank wear and cutting force signal. Tool wear is detected by monitoring of cutting signal. A monitoring procedure is shown in this paper. The influence of flank wear on cutting signal activity was examined. The results are presented in the form of graphs. The analysis of the cutting signal and flank wear curves provides useful indicators of unacceptable wear development in the tool.

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

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.12
• /
• pp.48-55
• /
• 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
• /
• v.6 no.4
• /
• pp.61-70
• /
• 1989

A new analytical method is proposed to monitor the chip form of cutting forces applying the techinque of signal process. Cutting experiments are carried out under various cutting conditons and cutting forces are measured in-processing through Tool Dynamometer. In this report, auto-correlation functions, frequency characteristics of dynamic force, high frequency distribution and Peak/RMS values are calculated from the measured cutting forces, and the concept of method is also discussed. The experimental results show that six types of the form of chips are possible to classify from the signal of cutting forces not related to cutting conditions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2001.04a
• /
• pp.192-196
• /
• 2001

This work describes the modeling of cutting process for feedback control based on signal processing in end-milling. Here, cutting force is used to design control model by a variety of schemes which are moving average, ensemble average, peak value, root mean square and analog low-pass filtering. It is expected that each model offers its own peculiar advantage in following cutting force control.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.58-63
• /
• 1993

The charactistics of AE(Acoustic Emission) signal is related to cutting conditions, tool materials and tool geometry in metal cutting. The tool geometry change which is derived from tool wear affects the source of AE signal in machining process. The relationship between AE signal and tool wear was experimentally investigated. THe value of RMS(Root Mean Sequare) and Amplitude of AE signal were increased in tool wear condition. Also the high value of Count per Hit and Count vs. Frequency was observed in this condtion. As a result, tool wear can be effectively detected by AE signal during cutting operation.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.3
• /
• pp.5-14
• /
• 1995

In recent years, the automization of cutting machine tools has been developed very fast. Hance, the in-process detection of cutting condition is very important for automatic manufacturing system in factory. Acoustic Emission(AE) has been widely used in monitoring the cutting conditions, because of high sensitivity of AE signal and low cost of AE equipment. This experimental study deals with the relations between AE signal, cutting force charcteristics and tool wear in the machining of SUS304. Face milling operation is used for the analysis between tool wear and AE signal.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.6 no.4
• /
• pp.124-129
• /
• 1997

The experimental investigation is mainly focused to detect tool wear by cutting signal measurements in multi-insert face milling SS 41 and STS 304. This research have investigated the effects on the insert number, which has relationship with mean-cutting force. AE(acoustic emission) signal, tool life and surface roughness in machining SS41 and STS 304. The cutting force and AE signal are monitored to analyse the cutting process, The surface roughness of the specimens machined by TiN coated tool with the various insert numbers measured at various cutting speeds, feed rates and depths of cut, The width of flank wear is also observed.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.4
• /
• pp.69-76
• /
• 1994

AE(Acoustic Emission) signal is correlated to workpiece material, cutting conditions and tool geometry during metal cutting. The relationship between AE signal and cutting parameters can be obtained by theoretical model and experiments. The value of CR(Count Rate) is nearly constant in stable cutting, but when the chatter vibration occours, the value of CR is rapidly increased due to the vibration deformation zone. By experimental signal processing of AE, it is more effective than by RMS(Root Mean Square) measurement to detect the threshold of chatter vibration by CR measurement.