Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding
In order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based...
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2013-01-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1155/2013/801964 |
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doaj-d1664b9c1e974729a4f81e7ed53023f12020-11-25T03:14:06ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322013-01-01510.1155/2013/80196410.1155_2013/801964Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection MoldingHang Liu0Hong Hu1Kai Leung Yung2Yan Xu3Xing Wei Zhang4 Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China Department of Industrial & Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Department of Industrial & Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong College of Engineering Shantou University, Shantou University, Shantou 515063, ChinaIn order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based on F -distribution is proposed in this paper. This adaptive Kalman filter can switch the system between the steady state and transient state by comparing the differences of input data in F -distribution. By resetting the Kalman gain and other relevant parameters, the adaptive function guarantees the convergence of the filtered signal during the tracking process and tracks the moments which sudden changes occur in the pressure signature. The simulation experiment results show that the method can reduce the effect of measurement noise more quickly and effectively. The method is proven to be effective for microinjection molding applications.https://doi.org/10.1155/2013/801964 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hang Liu Hong Hu Kai Leung Yung Yan Xu Xing Wei Zhang |
spellingShingle |
Hang Liu Hong Hu Kai Leung Yung Yan Xu Xing Wei Zhang Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding Advances in Mechanical Engineering |
author_facet |
Hang Liu Hong Hu Kai Leung Yung Yan Xu Xing Wei Zhang |
author_sort |
Hang Liu |
title |
Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding |
title_short |
Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding |
title_full |
Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding |
title_fullStr |
Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding |
title_full_unstemmed |
Melt Pressure Signature Tracking Using an Adaptive Kalman Filter in Microinjection Molding |
title_sort |
melt pressure signature tracking using an adaptive kalman filter in microinjection molding |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8132 |
publishDate |
2013-01-01 |
description |
In order to manufacture high quality microproducts, the precision control of injected plastic melt in the injection chamber during a microinjection process requires real-time tracking of the melt pressure when the melt passes through the nozzle. A novel type of adaptive Kalman filter algorithm based on F -distribution is proposed in this paper. This adaptive Kalman filter can switch the system between the steady state and transient state by comparing the differences of input data in F -distribution. By resetting the Kalman gain and other relevant parameters, the adaptive function guarantees the convergence of the filtered signal during the tracking process and tracks the moments which sudden changes occur in the pressure signature. The simulation experiment results show that the method can reduce the effect of measurement noise more quickly and effectively. The method is proven to be effective for microinjection molding applications. |
url |
https://doi.org/10.1155/2013/801964 |
work_keys_str_mv |
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