• 대한기계학회논문집A
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• 제25권6호
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• pp.972-979
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• 2001

Tool fracture index(TFI) was developed in order not only to detect tool fracture but also to predict the amount of tool fracture in face milling. TFI is calculated by using peak-to-valley values of cutting force acting on teeth and their ratio between the adjacent teeth. When the tool fractures, a large value of TFI proportional to the amount of tool fracture was obtained periodically and decreased gradually. It was found that TFI is independent of cutter runout and it almost does not vary during transient cutting such as cutting condition change during machining. The threshold of tool fracture can be analytically determined by TFI developed in this paper, because the magnitude of TFI was shown to be dependent on the ratio of the amount of tool fracture to feed per tooth and immersion ratio. It was possible to predict the amount of tool fracture in experiments by using the proposed TFI.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.411-415
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• 2004

In this paper, a double artificial neural network (ANN) approach and the efficient machining database building scheme are presented for the prediction of surface roughness in high-speed machining. In this approach, 4 machining parameters and used for the prediction of cutting force components, and the combinations of 4 parameters and the predicted cutting force components are finally used for the prediction of surface roughness. The experimental results comparing the these results with the predicted values using simple 4 input nodes have been also investigated to verify the effectiveness of the proposed approach.

• Composites Research
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• 제27권6호
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• pp.231-235
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• 2014

오토클레이브 성형은 성형제품의 품질은 우수하나 생산비용이 비싸다는 단점이 있다. 생산비용 중에서도 큰 비중을 차지하는 것이 성형툴의 제작공정이다. 따라서 본 연구에서는 생산비용 절감을 위한 선행 연구로서 성형툴의 재질 및 표면상태에 따라 L-shape 제품의 성형후 Spring-in을 Abaqus user subroutine을 이용하여 계산하였고, 열팽창계수와 마찰계수에 따른 결과를 나타내었다. 또한 성형툴 제작시 재질 및 표면상태의 기준점을 제시하여 생산비용을 줄이는데 기여하고자 한다.

• 한국정밀공학회지
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• 제13권12호
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• pp.151-157
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• 1996

The tool condition monitoring is one of the most important aspects to improve productivity and quality of workpiece. In this study, the wear of ceramic tool (A1$_{2}$0$_{3}$-TiC Series) cutting the hardened die material(SKD11) was investigated. Flank wear was more dominant than crater wear. Therefore the modeling of cutting force related to flank wear has been performed. The cutting force model was construct- ed by an assumption that the stress distribution on the tool face is affected by tool wear. The relationship between characteristics as cutting force and tool wear can be suggested by machining parameters depending on cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions on the flank wear are approximately in good agreement with experimental results.

• 한국공작기계학회논문집
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• 제12권5호
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• Nuclear Engineering and Technology
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• 제48권1호
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• pp.114-128
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• 2016

The development of operational performance indicators is of utmost importance for nuclear power plants, since they measure, track, and trend plant operation. Leading indicators are ideal for reducing the likelihood of consequential events. This paper describes the operational data analysis of the information contained in the Corrective Action Program. The methodology considers human error and organizational factors because of their large contribution to consequential events. The results include a tool developed from the data to be used for the identification, prediction, and reduction of the likelihood of significant consequential events. This tool is based on the resilience curve that was built from the plant's operational data. The stress is described by the number of unresolved condition reports. The strain is represented by the number of preventive maintenance tasks and other periodic work activities (i.e., baseline activities), as well as, closing open corrective actions assigned to different departments to resolve the condition reports (i.e., corrective action workload). Beyond the identified resilience threshold, the stress exceeds the station's ability to operate successfully and there is an increased likelihood that a consequential event will occur. A performance indicator is proposed to reduce the likelihood of consequential events at nuclear power plants.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2006년도 하계학술발표대회 논문집
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• pp.311-318
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• 2006

CFD simulation is a tool very useful to predict the generation and absorption of the contaminant from the construction materials for the single room condition. However, there is a limit in multi-room simulation for analyzing air movement and contaminant concentration at the condition that the door of each room was closed. A lot of network simulation tool were developed which can used to analyze the mass transfer and contaminant concentration as results in the multi-room condition. However, existing network simulation method was not able to analyze the change of the heating and cooling load with the ventilation as though the change of the indoor air-pollution density was predictable. In this study, new approach to predict heating/cooling load and indoor contaminant concentration will be reviewed. New indoor contaminant concentration module reviewed in this study wad coupled with existing ESP-r network simulation method. The validity of new approach will be analysed for comparison the results of simulation and field measurement results.

• Design & Manufacturing
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• 제16권4호
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• pp.46-51
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• 2022

The piercing process of creating holes in sheet metals for mechanical fastening generates high shear force. Real-time monitoring technology could predict tool damage and product defects due to this severe condition, but there are few applications for piercing high-strength aluminum. In this study, we analyzed the load signal to predict the punch's wear level during the process with a piezoelectric sensor installed piercing tool. Experiments were conducted on Al6061 T6 with a thickness of 3.0 mm using piercing punches whose edge angle was controlled by reflecting the wear level. The piercing load increases proportionally with the level of tool wear. For example, the maximum piercing load of the wear-shaped punch with the tip angle controlled at 6 degrees increased by 14% compared to the normal-shaped punch under the typical clearance of 6.7% of the aluminum piercing tool. In addition, the tool wear level increased compression during the down-stroke, which is caused by lateral force due to the decrease in the diameter of pierced holes. Our study showed the predictability of the wear level of punches through the recognition of changes in characteristic elements of the load signal during the piercing process.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 추계학술대회 논문집
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• pp.54-59
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• 1998

In order to minimize straightness error of deflected shafts, a automatically bending process control system is designed, fabricated, and studied. The multi-step straightening process and the three-point bending process are developed for the geometric adaptive straightness control. Load-deflection relationship, on-line identification of variations of material properties, on-line springback prediction, and studied for the three-point bending processes. Selection of a loading point supporting condition are derved form fuzzy inference and fuzzy self-learning method in the multi-step straighternign process. Automatic straightening machine is fabricated by using the develped ideas. Validity of the proposed system si verified through experiments.

• 대한기계학회논문집A
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• 제31권9호
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• pp.924-933
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• 2007

An impeller is a important part of turbo-machinery. It has a set of twisted surfaces because it consists of many blades. Five-axis machining is required to produce a impeller because of interference between tool and workpiece. It can obtain good surface integrity and high productivity. This paper proposes finish cutting method for machining impeller with 5-axis machining center and optimization of cutting condition by response surface method. Firstly, cutting methods are selected by consideration of operation characteristics. Secondly, response factors are determined as cutting time and cutting error for prediction of productivity. Experiments are projected by central composite design with axis point. Thirdly, regression linear models are estimated as single surface in the leading edge and as dual surface in the hub surface cutting. Finally, cutting conditions are optimized.