東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Multivariate process analysis for th...

Hazen, Daniel.

Linked to FindBook

Google Book

Amazon

博客來

Multivariate process analysis for the prediction of injection molded part quality.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariate process analysis for the prediction of injection molded part quality./
Author:	Hazen, Daniel.
Description:	78 p.
Notes:	Source: Masters Abstracts International, Volume: 45-02, page: 1073.
Contained By:	Masters Abstracts International45-02.
Subject:	Plastics Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1439502
ISBN:	9780542979125

Multivariate process analysis for the prediction of injection molded part quality.
Hazen, Daniel.

Multivariate process analysis for the prediction of injection molded part quality. - 78 p.

Source: Masters Abstracts International, Volume: 45-02, page: 1073.

Thesis (M.S.Eng.)--University of Massachusetts Lowell, 2007.

The injection molding process is a highly complex system in which multiple variables influence the final molded part quality. The influence and interactions of all these variables requires the use of a system that analyzes the process in a multivariate manner. This thesis investigates a multivariate approach to on-line quality monitoring and quality predictions that can be fully implemented in a fairly short time period. This multivariate approach consists of determining and monitoring the most important process data features that are generally available and correlating them to critical part dimensions. A multivariate model is then created from these correlations; parts are then compared to this model and accepted or rejected based on multivariate statistics.

ISBN: 9780542979125Subjects--Topical Terms:

1023683
Plastics Technology.

Multivariate process analysis for the prediction of injection molded part quality.
LDR:02277nmm 2200277 4500 001 1834637
005 20071127114959.5
008 130610s2007 eng d
020 $a 9780542979125
035 $a (UMI)AAI1439502
035 $a AAI1439502
040 $a UMI $c UMI
100 1 $a Hazen, Daniel. $3 1923277
245 1 0 $a Multivariate process analysis for the prediction of injection molded part quality.
300 $a 78 p.
500 $a Source: Masters Abstracts International, Volume: 45-02, page: 1073.
500 $a Adviser: David O. Kazmer.
502 $a Thesis (M.S.Eng.)--University of Massachusetts Lowell, 2007.
520 $a The injection molding process is a highly complex system in which multiple variables influence the final molded part quality. The influence and interactions of all these variables requires the use of a system that analyzes the process in a multivariate manner. This thesis investigates a multivariate approach to on-line quality monitoring and quality predictions that can be fully implemented in a fairly short time period. This multivariate approach consists of determining and monitoring the most important process data features that are generally available and correlating them to critical part dimensions. A multivariate model is then created from these correlations; parts are then compared to this model and accepted or rejected based on multivariate statistics.
520 $a The implemented research investigates the use of different process sensors and different signal data features with different molds and materials. The results determined that this approach can be implemented and used successfully. Not only can it be used successfully, but it can be utilized on a molding process while monitoring only the sensors that are typically included on most machines. Multivariate models were created based on acceptable molded product. Validation trials were then run in which unacceptable parts were intentionally created. The unacceptable parts were rejected while acceptable parts were accepted.
590 $a School code: 0111.
650 4 $a Plastics Technology. $3 1023683
690 $a 0795
710 2 0 $a University of Massachusetts Lowell. $3 1017839
773 0 $t Masters Abstracts International $g 45-02.
790 1 0 $a Kazmer, David O., $e advisor
790 $a 0111
791 $a M.S.Eng.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1439502