Using geometric shape variations to create an inverse model for a sheet metal process

Rolfe, Bernard, Cardew-Hall, M., Abdallah, S. and West, G. 2003, Using geometric shape variations to create an inverse model for a sheet metal process, Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture, vol. 217, no. 12, pp. 1665-1675.

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Title Using geometric shape variations to create an inverse model for a sheet metal process
Author(s) Rolfe, Bernard
Cardew-Hall, M.
Abdallah, S.
West, G.
Journal name Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture
Volume number 217
Issue number 12
Start page 1665
End page 1675
Publisher Mechanical Engineering Publications Ltd.
Place of publication London, England
Publication date 2003
ISSN 0954-4054
2041-2975
Keyword(s) sheet metal forming
inverse models
classification
pattern recognition
quality control
process control
Summary The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.
Language eng
Field of Research 091099 Manufacturing Engineering not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002040

Document type: Journal Article
Collection: School of Engineering and Technology
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