• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.775-776
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• 2007

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.108-114
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• 2007

A new cutting force estimating approach and machining state monitoring examples are presented which uses a cylindrical displacement sensor built into the spindle. To identify the tool-spindle system dynamics with frequency up to 2 kHz, a home-built electro-magnetic exciter is used. The result is used to build an algorithm to extract the dynamic cutting force signal from the spindle error motion; because the built-in spindle sensor signal contains both spindle-tool dynamics and tool-workpiece interactions. This sensor is very sensitive and can measure broadband signal without affecting the system dynamics. The main characteristic is that it is designed so that the measurement is irrelevant to the geometric errors by covering the entire circumferential area between the target and sensor. It is also very simple to be installed. Usually the spindle front cover part is copied and replaced with a new one with this sensor added. It gives valuable information about the operating condition of the spindle at any time. It can be used to monitor cutting force and chatter vibration, to predict roughness and to compensate the form error by overriding spindle speed or feed rate. This approach is particularly useful in monitoring a high speed machining process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.178-186
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• 2009

Most of automotive plastic parts are injection molding products. Inspection of total product is impossible, because number of product to inspect is too many and various. Condition-based Monitoring was proposed to decrease cost and time for inspecting. In this research, a system that predicts quality of part at fabrication point of time, and confirms informations through the internet was developed. Cavity sensors were installed inside of mold, and gathered signals as measuring, and through this process Sensor-based Monitoring system can be observed manufacturing of a part. Monitoring system transmits signals to client through the internet, and finally developed system provides manufacturing informations and predictions of quality as web-based monitoring.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.51-69
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• 2016

Monitoring the performance and estimating the remaining useful life of aging civil infrastructure in the United States has been identified as a major objective in the civil engineering community. Structural health monitoring has emerged as a central tool to fulfill this objective. This paper presents a review of the major structural monitoring programs that have been recently implemented in the United States, focusing on the integrity and performance assessment of large-scale structural systems. Applications where response data from a monitoring program have been used to detect and correct structural deficiencies are highlighted. These applications include (but are not limited to): i) Post-earthquake damage assessment of buildings and bridges; ii) Monitoring of cables vibration in cable-stayed bridges; iii) Evaluation of the effectiveness of technologies for retrofit and seismic protection, such as base isolation systems; and iv) Structural damage assessment of bridges after impact loads resulting from ship collisions. These and many other applications show that a structural health monitoring program is a powerful tool for structural damage and condition assessment, that can be used as part of a comprehensive decision-making process about possible actions that can be undertaken in a large-scale civil infrastructure system after potentially damaging events.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1076-1077
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• 2007

CMS(Condition Monitoring System) is a useful tool to predict the defect of machine condition, for example, Motor, Bearing, Gear, Fan, etc. And, recently CMS is very important on plant. In this paper, describe the bearing electric arcing with example.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.269-273
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• 1997

In order to maker 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 fracture during turning operation. To develop economic sensing and identification 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 Propulsion Engineers
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• v.17 no.1
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• pp.80-88
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• 2013

In currently developed engine condition monitoring systems, most field engine maintenance engineers have difficulties to use them in fields due to complexity, unpractical use, lack of understanding, etc. Therefore a practical usable engine condition monitoring system must be needed. This work proposes a practical performance condition monitoring of a small turbojet engine through comparing between the on-line performance monitoring data and the initial clean performance data calculated by the base engine performance model. Moreover the proposed monitoring system checks the gas path components' on-line health condition through comparing the component performance characteristics between the running engine represented as a deteriorated engine or a degraded engine and the base engine performance model represented as a clean engine. The proposed condition monitoring system is coded in a friendly GUI type program for easy practical application by a commercial tool, MATLAB/SIMULINK and LabVIEW.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.71-75
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• 1993

The application of artificial neural network forcondition monitoring and diagnosis of milling tools was introduced. To detect the conditions of milling tools, the monitoring wywtem consists of three phases: "preparation phase", "learning phase", "production phase". The conditions of milling tools were categorized into the three states. "normal", "warning", "abnormal". The dectection of tool condition, in this paper, could be successfully performed by monitoring the variation of power spectrum on Y dirctional cutting force.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.209-212
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• 2002

The wear process of end mill is a so complicated process that a more reliable technique is required for the monitoring and controling the tool life and its performance. This research presents a new tool wear monitoring method based on the sound signal generated on the machining. The experiment carried out continuous-side-milling for 4 cases using the high-speed steel slot drill under wet condition. The sound pressure was measured at 0.5m from the cutting zone by a dynamic microphone, and was analyzed at frequency domain. The tooth passing frequency appears as a harmonics form, and end mill wear is related with the first harmonic. It can be concluded from the result that the tool wear is correlate with the intensity of the measured sound at tooth passing frequency estimation of end mill wear using sound is possible through frequency analysis at tooth passing frequency under the given circumstances.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.