• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.3
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• pp.36-43
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and breakage during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.142-147
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• 2012

Machining tool conditions directly affect to quality of product and productivity of manufacturing. Many researches performed for tool condition monitoring in machining process to improve quality and productivity. Conventional methods use characteristics of signal for cutting force, motor current consumption, vibration of machine tools and machining sound. Recently, diameter of machining tool is become smaller for minimizing of mechanical parts. Tool condition monitoring using conventional methods are relatively difficult because micro machining using small diameter tool has low machining load and high cutting speed. These days, the direct monitoring for tool conditions using vision system is performed actively. But, vision system is affected by external conditions such as back ground of image and illumination. In this study, minimizing technology of external conditions using distribution analysis of image data are developed in micro machining using small diameter drill and tap. The image data is gathered from vision system. Several sets of experiment results are performed to verify the characteristics of the proposed machining technology.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1069-1074
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• 2008

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the shaving process using beta probability distribution in order to extract the effective features. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating parameters of beta probability distribution based on method of moments. The usefulness of features obtained from the proposed method was evaluated and discussed.

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.50-57
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for chatter, tool wear and breakage during turning operation. To develop economic sensing and identiffication methods for turning processes, sound pressure measurement and digital signal processing technique were proposed. We suppressed chatter by stability control methodology, which was studied through manipulation of spindle speeds regarding to chatter frequencies. It was shown that tool wear and fracture were identified and to be estimated by using the wear indices. The validity of the proposed system was confirmed through the large number of cutting tests.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.148-152
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• 2000

Detection of tool wear is very important in automated manufacturing. This paper presents tool condition monitoring system based on the wavelet analysis of the AC servo motro current in drilling and milling process. The current measurement system is relatively simple and its mounting will not affect machining operations. The discrete wavelet transform was used to decompose the current signal of a spindle AC servo motor in time - frequency domain. The feature vectors were extracted from the decomposed signals and compared for normal and wear condition. The results show the possibility for the effective application of wavelet analysis to tool condition monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.923-924
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• 2008

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.