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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Main Authors: Hang Liu, Hong Hu, Kai Leung Yung, Yan Xu, Xing Wei Zhang
Format: Article
Language:English
Published: SAGE Publishing 2013-01-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1155/2013/801964
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spelling 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
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AT honghu meltpressuresignaturetrackingusinganadaptivekalmanfilterinmicroinjectionmolding
AT kaileungyung meltpressuresignaturetrackingusinganadaptivekalmanfilterinmicroinjectionmolding
AT yanxu meltpressuresignaturetrackingusinganadaptivekalmanfilterinmicroinjectionmolding
AT xingweizhang meltpressuresignaturetrackingusinganadaptivekalmanfilterinmicroinjectionmolding
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