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Modelling for injection moulding process based on principal component regression method

Gu, Nong, Creighton, Doug and Nahavandi, Saeid 2010, Modelling for injection moulding process based on principal component regression method, in AMPT 2010 : AIP Conference Proceedings : International Conference on Advances in Materials and Processing Technologies, American Institue of Physics (API), Melville, New York, pp. 695-700.

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Title Modelling for injection moulding process based on principal component regression method
Author(s) Gu, Nong
Creighton, Doug
Nahavandi, Saeid
Conference name International Conference on Advances in Materials and Processing Technologies (2010 : Paris, France)
Conference location Paris, France
Conference dates 24-27 Oct. 2010
Title of proceedings AMPT 2010 : AIP Conference Proceedings : International Conference on Advances in Materials and Processing Technologies
Editor(s) Chinesta, Francesco
Chastel, Yvan
El Mansori, Mohamed
Publication date 2010
Series AIP Conference Proceedings, v.1315 Part Two
Start page 695
End page 700
Total pages 6
Publisher American Institue of Physics (API)
Place of publication Melville, New York
Summary Determination of the optimal operating condition for moulding process has been of special interest for many researchers. To determine the optimal setting, one has to derive the model of injection moulding process first which is able to map the relationship between the input process control factors and output responses. One of most popular modeling techniques is the linear least square regression due to its effectiveness and completeness. However, the least square regression was found to be very sensitive to the outliers and failed to provide a reliable model if the control variables are highly related with each other. To address this problem, a new modeling method based on principal component regression was proposed in this paper. The distinguished feature of our proposed method is it does not only consider the variance of covariance matrix of control variables but also consider the correlation coefficient between control variables and target variables to be optimised. Such a modelling method has been implemented into a commercial optimisation software and field test results demonstrated the performance of the proposed modelling method.
ISBN 9780735408715
ISSN 0094-243X
Language eng
Field of Research 091006 Manufacturing Processes and Technologies (excl Textiles)
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E3 Extract of paper
HERDC collection year 2010
Copyright notice ©2010, American Institute of Physics
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034569

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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