• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.1
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• pp.156-161
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• 1992

In this research a monitoring system for turning using NC information and the current of feed motor as a monitoring signal was developed. The overall system consists of modules such as learning process, NC data transmission, generation of forecast information, signal acquisition, monitoring and post process. In the learning process, the reference data and the cutting force equation necessary for monitoring are obtained from the accumulated monitoring results. In the generation of forecast information, the information of forecasted cutting forces is acquired from the cutting force equation and NC program and appended to each NC block as a monitor code. Reliability of monitoring is improved by using the monitor code in the real-time monitoring. Monitoring module is divided into two parts : the off-line monitoring where errors of NC program are checked and the on-line monitoring where the level of motor current is monitored during cutting operations. If the actual current level exceeds the limit value provided by the monitor code in the level monitoring, it is recognized as abnormal. In the event of abnormal status, the post processor sends the emergency stop signal to NC controller to stop the operation. Actual experiments have shown that the developed monitoring system works well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.