• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.142-147
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• 2012

Machining tool conditions directly affect to quality of product and productivity of manufacturing. Many researches performed for tool condition monitoring in machining process to improve quality and productivity. Conventional methods use characteristics of signal for cutting force, motor current consumption, vibration of machine tools and machining sound. Recently, diameter of machining tool is become smaller for minimizing of mechanical parts. Tool condition monitoring using conventional methods are relatively difficult because micro machining using small diameter tool has low machining load and high cutting speed. These days, the direct monitoring for tool conditions using vision system is performed actively. But, vision system is affected by external conditions such as back ground of image and illumination. In this study, minimizing technology of external conditions using distribution analysis of image data are developed in micro machining using small diameter drill and tap. The image data is gathered from vision system. Several sets of experiment results are performed to verify the characteristics of the proposed machining technology.

• Transactions of Materials Processing
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• v.15 no.1 s.82
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• pp.82-88
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• 2006

In this paper, the web-based monitoring system is developed for the effective process monitoring of mold manufacturing using web. In developed system, the cutting force for monitoring the manufacturing condition is measured using hall-sensor that is low cost and useful to be installed in a machine tool indirectly. Specially, the current of main spindle in a machine tool is converted into cutting force by various experiments. For effective web-based monitoring, the program which runs in the local computer of client is made to exchange message between a server and a client by making of ActiveX control and the result of manufacturing is shown on web-browser by Ch language. The developed system in this study is the foundation of establishing E-manufacturing in mold factory.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.1
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• pp.43-50
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• 1996

From a manufacturing standpoint it would be desirable to monitor the degradation of drawing die, that is essential for the maintenance of die quality, the evaluation of product integrity and the reducing scrap. Acoustic emission is powerful method in monitoring fine wire drawing process, especially in detecting the die fracture at early stage. Experiments also suggested that acoustic emission sigals contained valuable information regarding the stage of a drawing process such as the surface appearance of products and the condition of lubrication. These informations are AE monitoring techniques a possible tool in monitoring the drawing process operation. In order to approach this, this paper discusses the nature of acoustic emission signal presented which illustrate the effects of wire and die material, lubricants, and drawing speed on the generation and the mean voltage level of acoustic emission signal. From these experimental, results, we understanded controlling factors of acoustic emission generation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.34-40
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• 2003

This paper describes evaluation and monitoring methods of machining characteristics for developed micro grooving machine. Experiments were conducted under various process conditions such as spindle revolution speed, feed rates and depth of groove V and U shape of blade and STD11 were used in this experiment. The status of grooving was evaluated through analysis of the Acoustic emission (AE) signal resulted in each process condition. Based on the analysis, this paper examined the possibility of monitoring adapting fuzzy logic. In conclusion, this paper presented the possibility of monitoring in process adapting AE technology and appropriate micro grooving condition.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.74-80
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• 1999

Recently, manufacturing work has been transformed to small scale production form with various types to act up to user's expectation from mass production with a little items required in the past. Then FMS using NC type machinaries has been applied actively also in domestic manufacturing line to meet thus tendency, but there are many machining troubles occured during work process not be settled yet. Nowdays high efficiently has been required no less than high precision in grinding work for the improvement of productivity. In this study, to represent more advance FMS can be adapted to thus situation In-process type monitoring method using AE and Current sensors is suggested to investigate the machining condition in grinding process. As results form this experimental study, it is recognized well that grinding conditions and dressing point of in time cab be estimated effectively using monitoring method suggested. Furthermore, surface shape of grinding wheel on voluntary point of in time can be predicted indirectly through the observation and comparison of AE signal waveform obtained as performance of continuous dressing work.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.1
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• pp.31-36
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• 2009

Micro-machining technology has been adopted for shape accuracy of micrometer and sub-micrometer scale, surface roughness of tens nanometer in industries. In micro-machining process the quality of machined surface is derived from machining condition and tooling. This paper investigates AE(acoustic emission) and cutting force signals according to machined surface quality related to machining condition. Machined surface quality was analyzed by the AE and cutting force parameter which reflect surface morphology. The characteristics of signal were extracted for process optimization by monitoring both the tool condition and the machined surface texture in micro end milling process.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.739-742
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• 2014

In the process of MLCC manufacturing, MLCC stacking process is the key process of making high quality MLCC. Since MLCC is small components, the entire process of MLCC stacking process is minute and sensitive to micro errors. To prevent micro error, we suggest condition-based monitoring which quantifies error based on feature extraction and quantifying error method. As results, it has been shown that the suggested algorithm has effectiveness of condition based monitoring of MLCC stacker.