• 지질공학
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• 제19권4호
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• pp.483-492
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• 2009

터널 및 지하구조물 시공 중 굴착 대상 지반에 대한 정확한 정보 획득은 작업의 효율성과 안전성을 위해 매우 중요하다. 본 연구에서는 굴착 중 굴진 대상 암반의 지질 구조를 신속하고 정확하게 감지하기 위하여 천공탐사 기법을 이용하였다. 유압 착암기 천공 시 발생하는 천공속도, 회전압, 피드압 등의 기계량을 측정하여 분석하였으며, 이를 통해 암석과 지질 구조적 특성에 의해 변화되는 암반 특성을 평가 하였다. 터널 시공현장에서 굴착 중 획득된 천공데이터 분석에 의해 평가된 암반 정보는 굴착 전 수행된 선진수평시추 및 TSP 탐사 결과와 비교하여 신뢰성을 검토하였으며, 그 결과 천공 데이터의 변화가 암반 특성 및 불연속면 예측을 위해 효율적으로 활용될 수 있음을 확인하였다.

• 동력기계공학회지
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• 제2권2호
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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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.

• Steel and Composite Structures
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• 제21권3호
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• pp.573-588
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• 2016

Carbon Fiber Reinforced Plastic (CFRP) and Titanium Alloy (Ti6Al4V) stack, extensively used in aerospace structural components are assembled by fasteners and the holes are made using drilling process. Drilling of stack in one shot is a complicated process due to dissimilarity in the material properties. It is vital to have optimal machining condition and tool geometry for better hole quality and tool life. In this study the tool wear and hole quality were analysed by experimental analysis using three modified twist drills and online tool condition monitoring using Acoustics Emission (AE) sensor. Helix angle and point angle influence tool performance and cutting force. It was found that a tool geometry (TG1) with high helix angle of $35^{\circ}$ with low point angle $130^{\circ}$ results in reduction in thrust force of 150-500 N range but the TG2 also perform almost similar to TG1, but when compared with the AErms voltage generated during drilling it was found that progressive rise in voltage in TG1 is less with respect to TG2 which can be attributed to tool life. In process wear monitoring was done using crest factor as monitoring index. AErms voltage were measured and correlated with the performance of the drills.

• 한국공작기계학회논문집
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• 제17권4호
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• pp.95-103
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• 2008

Diamond core drills are applied to drill difficult-to-cut materials. This paper proposes basic understanding of ceramic drilling mechanics and characteristics of main factors affecting tool life, tool wear, cutting force, and chipping thickness. In contrast to conventional drilling, the core drilling process make deep grooves on the workpiece. One difficulty of it is the evacuation of chips from the drilled groove. As the drilling depth increases, an increased amount of chips tend to cluster together and clog the groove. Eventually severe wear develops and diamond grits are separated from the drill body. To relieve the clogging problem and to evacuate chips from the groove easily, the helical drilling process is applied for the core drilling process. To analyze drilling characteristics and derive optimal drilling conditions, tool life, tool wear, cutting force, and chipping thickness are quantified through the monitoring system and the Taguchi method. Mathematical models for the tool life and chipping thickness are derived from the response surface method. Optimal drilling database has been constructed through the experimental models.

• 비파괴검사학회지
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• 제28권3호
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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.

• 한국공작기계학회논문집
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• 제12권3호
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• pp.8-16
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• 2003

Monitoring of the drill wear :md hole quality change is conducted during the drilling process. Cutting force measured by tool dynamometer is a evident feature estimating abnormal state of drilling. One major difficulty in using tool dynamometer is that the work-piece must be mounted on the dynamometer, and thus the machining process is disturbed and discontinuous. Acoustic transducer do not disturb the normal machining process and provide a relatively easy way to monitor a machining process for industrial application. for this advantage, AE signal is used to estimate the abnormal fate. In this study vision system is used to detect flank wear tendency and hole quality, there are many formal factors in hole quality decision circularity, cylindricity, straightness, and so of but these are difficult to measure in on-line monitoring. The movement of hole center and increasement of hole diameter is presented to determine hole quality. As the results of this experiment AE RMS signal and measurements by vision system are shorn the similar tendency as abnormal state of drilling.

• 한국안전학회지
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• 제16권4호
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• pp.47-51
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• 2001

The carbon fiber reinforced plastics(CFRP) have been widely used in aircraft and spacecraft structures as well as sports goods because it has high specific strength, high specific stiffness and low coefficient of thermal expansion. Machining of CFRP poses problems not frequently seen for metals due to the nonhomogeneity, anisotropy, and abrasive characteristics of CFRP. Delamination is a common problem faced while drilling holes in CFRP using conventional drilling. Therefore, AE characteristics related to drilling damage process of unidirectional and [0/90/]s crossply laminate composite was studied. Also drilling damage like the delamination was observed by video camera in real time monitoring technique. From the results, we basically found the relationships between the delamination from drilling and AE characteristics for CFRP composites.

• 한국정밀공학회지
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• 제22권4호
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• pp.37-43
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• 2005

Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT-based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.

• 한국공작기계학회논문집
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• 제11권2호
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• pp.103-111
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• 2002

Out of all metal-cutting process, 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. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by 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. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.