• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1997년도 추계학술대회 논문집
• /
• pp.332-335
• /
• 1997

With the advance of precision technique, the optical system is more precise and complex and the machining method of optical element which is composed of micro-grooves is developed. Especially, the method of micro-grooving using diamond tool is used widely owing to many merit, but has problems of damage of surface roughness due to tool wear and tool fracture. This paper deals with state monitoring using AE RMS in the micro-grooving. The change of AE RMS is very small with increment of cutting velocity and depth of cut. In spite of constance magnitude of principal force in machining using diamond tool of tool wear and tool fracture, AE RMS is highly fluctuated. Because changing of cutting state has relevance to surface roughness profile, surface toughness profile is expected using AE RMS.

• 한국생산제조학회지
• /
• 제7권3호
• /
• pp.14-21
• /
• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• 한국기계가공학회지
• /
• 제16권5호
• /
• pp.31-39
• /
• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• 한국정밀공학회지
• /
• 제11권1호
• /
• pp.138-149
• /
• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

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

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2004년도 추계학술대회 논문집
• /
• pp.334-339
• /
• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• 한국정밀공학회지
• /
• 제15권11호
• /
• pp.32-38
• /
• 1998

For the purpose of monitoring the abnormal state in proportion to cutting in automatic production process, the 3 kinds of specimens different from mechanical properties by austempering through temperature variation were manufactured, and the effects of tool wear on thrust and AE RMS was analyzed with sequential drilling in in-process. When the ADI specimens were drilled, the relationship of thrust and AE RMS with flank wear was studied through experiments, and it is confirmed that the reliable wear state is able to be monitored by using these signals. It was shown that thrust and AE RMS increased slowly till flank wear reached to V$_{B}$ = 0.25mm, and they increased steeply over the value. The effective tool exchange time was able to be pre-estimated by using this fact. It was validated that the tool breakage was able to be detected on the real time by monitoring in in-process.s.

• 한국생산제조학회지
• /
• 제21권1호
• /
• pp.33-39
• /
• 2012

The tool wear and failure in automatic production system directly influences the quality and productivity of a product, thus it is essential to monitor the tool state in real time. For such purpose, an ART2-based remote monitoring system has been developed to predict the appropriate tool change time in accordance with the tool wear, and this study aims to experimently find the relationship between the tool wear and the monitoring signals in cutting processes. Also, the roughness of workpiece according to the wool wear is examined. Here, the tool wear is indirectly monitored by signals from a vibration senor attached to a machining center. and the wear dimension is measured by a microscope at the start, midways and the end of a cutting process. A series of experiments are carried out with various feedrates and spindle speeds, and the results show that the sensor signal properly represents the degree of wear of a tool being used, and the roughnesses measured has direct relation with the tool wear dimension. Thus, it is concluded that the monitoring signals from the vibration sensor can be used as a useful measure for the tool wear monitoring.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 추계학술대회 논문집
• /
• pp.888-891
• /
• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2003년도 춘계학술대회 논문집
• /
• pp.472-477
• /
• 2003

Spindle state monitoring is getting more and more important according to the technology trend of spindle that is accurate and automated. Spindle state monitoring is to measure the state of rotation vibrations. The spindle rotation error motion detected by sensing device includes rotation object's unbalance, external forced vibrations, shape error of spindle, as well as measuring error of monitoring device. In this paper, we have inspected the runout characteristics. Also, we introduce the way to exclude the runout element that appear while you monitor a spindle state.