103 0

Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model

Title
Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model
Author
김진수
Keywords
injection molding; cavity pressure; interpretable machine learning
Issue Date
2021-09
Publisher
MDPI
Citation
POLYMERS; OCT 2021, 13 19, p3297 18p.
Abstract
The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.
URI
https://www.proquest.com/docview/2581014820?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/170433
ISSN
20734360
DOI
10.3390/polym13193297
Appears in Collections:
RESEARCH INSTITUTE[E](부설연구소) > RESEARCH INSTITUTE OF ENGINEERING & TECHNOLOGY(공학기술연구소) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE