• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.599-600
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• 2012

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.701-702
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• 2013

• 한국정밀공학회지
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• 제31권7호
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• pp.561-567
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• 2014

Precision injection mold is an essential element in order to manufacture small and precision plastic lenses used for phone camera. There are many critical factors to meet the requested specifications of high quality plastic lenses. One of the main issues to realize high quality is minimizing decenter value, which becomes more critical as pixel numbers increases. This study suggests the method to minimize decenter value by modifying ejecting structure of the mold. Decenter value of injection-molded lens decreased to 1 ${\mu}m$ level from 5 ${\mu}m$ by applying suggested ejecting method. Also, we also developed BIS (Built-in Sensor) based smart mold system, which has pressure and temperature sensors inside of the mold. Pressure and temperature profiles from cavities are obtained and can be used for deduction of optimal injection molding condition, filling imbalance evaluation, status monitoring of injection molding and prediction of lens quality.

• 한국정밀공학회지
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• 제33권1호
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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.