• Journal of the Korean Society for Precision Engineering
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• v.33 no.1
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• pp.23-29
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• 2016

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.433-434
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• 2012

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.343-348
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• 2013

In line with the advances in factory automation, various pieces of equipment are now operated in batch processes controlled by computers. However, many kinds of faults can occur in complicated and large systems, which can result in low productivity and economic loss. The reliability and safety of systems have been studied because of the difficulty of determining the severity and location of faults. Therefore, it is necessary to detect and diagnose such faults in order to guarantee the reliability and safety of the equipment. In this paper, a diagnosis method for the ball bearings of a hydraulic pump is applied using a vibration signal for the maintenance of injection molding equipment. The bearings' defects are selected as a main failure mode through a failure mode and effect analysis (FMEA). Usually, there are nonlinear and impulse components of vibration in a ball bearing with faults. For the effective fault diagnosis of a ball bearing, nonlinear diagnostic methods and time-frequency analysis are applied, in addition to the methods currently used, such as power spectrum, time series analysis, and statistical methods. As a result of this study, a failure diagnosis system is provided that is useful even for non-experts. This is a condition-based method that makes it possible to resolve problems in a timely and economical way, in contrast to the prior method, which required regular but wasteful maintenance based on the experience of expensive external experts.