Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach.: An article from: Polymer Engineering and Science
Book Details
Author(s)Jian Zhou, Lih-Sheng Turng
PublisherThomson Gale
ISBN / ASINB000R37ZTO
ISBN-13978B000R37ZT8
AvailabilityAvailable for download now
Sales Rank10,301,741
MarketplaceUnited States 🇺🇸
Description
This digital document is an article from Polymer Engineering and Science, published by Thomson Gale on May 1, 2007. The length of the article is 6302 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: This article presents an integrated, simulation-based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn-around time and adequate accuracy for substituting time-consuming computer simulations during system-level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684-694, 2007. [c] 2007 Society of Plastics Engineers
Citation Details
Title: Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach.
Author: Jian Zhou
Publication:Polymer Engineering and Science (Magazine/Journal)
Date: May 1, 2007
Publisher: Thomson Gale
Volume: 47 Issue: 5 Page: 684(11)
Distributed by Thomson Gale
From the author: This article presents an integrated, simulation-based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn-around time and adequate accuracy for substituting time-consuming computer simulations during system-level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684-694, 2007. [c] 2007 Society of Plastics Engineers
Citation Details
Title: Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach.
Author: Jian Zhou
Publication:Polymer Engineering and Science (Magazine/Journal)
Date: May 1, 2007
Publisher: Thomson Gale
Volume: 47 Issue: 5 Page: 684(11)
Distributed by Thomson Gale
