Injection molding product weight: online prediction and control based on a nonlinear principal component regression model.: An article from: Polymer Engineering and Science

This digital document is an article from Polymer Engineering and Science, published by Thomson Gale on April 1, 2006. The length of the article is 5166 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

From the author: Weight is an important quality characteristic of injection-molding products. The current work focuses on the online prediction and closed-loop control of the product weight. Previous researchers used the process set-points as the inputs to establish weight prediction model. These models cannot reflect the weight variations at a given setting. In this study, an online weight prediction model has been developed, with the process variable trajectories as the inputs, using a principal component regression (PCR) model. A nonlinear enhancement has been made to improve the prediction accuracy of the PCR weight model. Based on such an online prediction, a closed-loop weight control system has been developed and tested experimentally. POLYM. ENG. SCI., 46:540-548, 2006. [c] 2006 Society of Plastics Engineers

Citation Details
Title: Injection molding product weight: online prediction and control based on a nonlinear principal component regression model.
Author: Yi Yang
Publication:Polymer Engineering and Science (Magazine/Journal)
Date: April 1, 2006
Publisher: Thomson Gale
Volume: 46 Issue: 4 Page: 540(9)

Distributed by Thomson Gale