• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.68-75
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• 2000

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 tool wear, which affects process efficiency and product quality, and implementing automatic tool 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. Cutting force components are divided into static and dynamic components in this paper, and the static components 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 disparities are defined in this paper, and the relationships between normalized disparity and flank wear are established. Finally, Artificial neural network is used to learn these relationships and detect tool wear. According to the proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• 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.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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.1951-1962
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• 1993

The creation process of a typical machined surface is treated here as a dynamic system. An investigation is carried out to establish a relationship between the characteristics of cutting force fluctuations that cause vibration response of the tool-workpiece system and the formation of surface in face cutting by sintered carbide cutting tool. Cutting force is measured and analyzed in frequency domain. The power spectral densities of cutting force give a useful information in surface generation and it can be used to find out the control factor of surface roughness. The terms, PSD ratio & Normalized spindle frequency PSD, are defined and when the value of power in spindle frequency is absolutely little but relatively large, it is obtained high accuracy surface roughness. The aim of this research is to find surface profile by measured and analyzed cutting force signals. The simulation of surface generation gives the comprechension of its mechanism and help to predict and control the surface quality. In this study, it is suggested what informations about surface generation can be acquired from the cuttuing force signal and an way of generating a better surface.

• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.1
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• pp.28-37
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• 2004

An on-line monitoring system of endmill failure such as weal, chipping, and fracture is developed using AE, cutting force Characteristic variations of AE and cutting force signals due to endmill failure are identified as follows. When endmill fracture occurs, AE count rate shows a rapid Increase in conjunction with a subsequent decrease while a standard deviation of the principal cutting force Increases significantly. The increase of AE count rate precedes the Increase of standard deviation of principal cutting force. Chipping results in relatively small increase and decrease of AE count rate without any significant variation of the cutting force Gradual increase of AE count rate and mean principal cutting force are Identified to be related with the wear of cutter. A cutter fracture detection algorithm is developed based on the present results. The signals me normalized to enhance the applicability of the algorithm to Wide those of fresh cutters, and qualitative characteristics of AE signals encountered at the moment of fracture are employed. It is demonstrated that the algorithm can detect the cutter fracture successfully.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.04a
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• pp.438-442
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• 1994

절삭가공에서 공구의 마멸은 생산의 최적화에 영향을 주는 가장 중요한 요소중의 하나라고할 수 있다. 따라서 생산시스템이 자동화되고 유연성 및 생산성이 증대되면서, 공구의 수명이 끝났을 때의 공구교환을 위한 최적 의사 결정전략(Decision making stratagy)은 그 중요성이 점차 커지고 있다. 한편, 공구는 마멸의 진행에 따라 그 수명을 예측하여 교환해 주는 것이 바람직하다. 그러나 공구의 마멸은 여러가지 요인들의 복합적 작용에의해 발생하는 현상 이므로 그것을 정확히 예측한다는 것은 많은 어려움이 있다. 본 연구에서는 절삭력을 이용하여 공구의 여유면마멸 (Flank wear)과 경사면마멸을 감지하고자 한다 먼저 절삭력을 정적인 성분(Static component)과 동적인 성분(Dynamic component)로 구분하여 공구의 마멸을 감지하는데 이용하였다. 절삭력의 정적인 성분은 절삭조건의 변화에 대해 정규 화된(Normalized) 절삭력으로 모델링하여 공구의 여유면 마멸을 감지 하였다. 또한 공구의 경사면마멸이 발생한 경우에는 절삭력의 동적인 성분이 크게 변화함을 알 수 있었다.