Using geometric shape variations to create an inverse model for a sheet metal process
Abstract 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 variati...
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| Published in | Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture Vol. 217; no. 12; pp. 1665 - 1675 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London, England
SAGE Publications
01.01.2003
Mechanical Engineering Publications SAGE PUBLICATIONS, INC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0954-4054 2041-2975 |
| DOI | 10.1243/095440503772680596 |
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| Abstract | Abstract
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. |
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| AbstractList | 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. [PUBLICATION ABSTRACT] 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. 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. Abstract 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. |
| Author | West, G A W Abdallah, S M Cardew-Hall, M J Rolfe, B F |
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| Keywords | sheet metal forming quality control process control classification inverse models pattern recognition |
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| References | Ghouati, Gelin 2001; 21 de Villiers, Barnard 1992; 4 Yang, Nezu 1998; 79 Rolfe, Cardew-Hall, Abdallah, West 2001; 215 Cootes, Taylor, Cooper, Graham 1995; 61 Guo, Batoz, Naceur, Bouabdallah, Mercier, Barlet 2000; 78 Park, Yoon, Yang, Kim 1999; 41 bibr7-095440503772680596 Doolan M. (bibr4-095440503772680596) 2000 Zeng M. (bibr5-095440503772680596) 2000 Schalkoff R. (bibr11-095440503772680596) 1992 Fukunaga K. (bibr12-095440503772680596) 1990 bibr13-095440503772680596 bibr10-095440503772680596 bibr14-095440503772680596 bibr2-095440503772680596 bibr3-095440503772680596 bibr1-095440503772680596 bibr6-095440503772680596 bibr8-095440503772680596 bibr9-095440503772680596 |
| References_xml | – volume: 79 start-page: 86 year: 1998 end-page: 93 article-title: Application of an inverse FE approach in the concurrent design of sheet stamping publication-title: J. Mater. Processing Technol. – volume: 21 start-page: 57 year: 2001 end-page: 68 article-title: A finite element-based identification method for complex metallic material behaviours publication-title: Comput. Mater. Sci. – volume: 215 start-page: 1229 issue: B9 year: 2001 end-page: 1240 article-title: Geometric shape errors in forging: Developing a metric and an inverse model publication-title: Proc. Instn Mech. Engrs, Part B: J. Engineering Manufacture – volume: 41 start-page: 1217 year: 1999 end-page: 1232 article-title: Optimum blank design in sheet metal forming by the deformation path iteration method publication-title: Int. J. Mech. Sci. – volume: 61 start-page: 38 issue: 1 year: 1995 end-page: 59 article-title: Active shape models—their training and application publication-title: CVGIP: Computer Vision and Image Understanding – volume: 78 start-page: 133 year: 2000 end-page: 148 article-title: Recent developments on the analysis and optimum design of sheet metal forming parts using a simplified inverse approach publication-title: Computers and Structs – volume: 4 start-page: 136 issue: 1 year: 1992 end-page: 141 article-title: Backpropagation neural nets with one and two hidden layers publication-title: IEEE Trans, on Neural Networks – ident: bibr3-095440503772680596 – ident: bibr14-095440503772680596 doi: 10.1243/0954405011519295 – ident: bibr9-095440503772680596 doi: 10.1016/S0927-0256(00)00215-9 – volume-title: Introduction to Statistical Pattern Recognition year: 1990 ident: bibr12-095440503772680596 – ident: bibr1-095440503772680596 – ident: bibr10-095440503772680596 doi: 10.1006/cviu.1995.1004 – volume-title: ICME 2000 year: 2000 ident: bibr4-095440503772680596 – ident: bibr8-095440503772680596 doi: 10.1016/S0020-7403(98)00084-8 – ident: bibr7-095440503772680596 doi: 10.1016/S0045-7949(00)00095-X – volume-title: Pattern Recognition: Statistical, Structural and Neural Approaches year: 1992 ident: bibr11-095440503772680596 – ident: bibr6-095440503772680596 doi: 10.1016/S0924-0136(97)00386-5 – ident: bibr13-095440503772680596 doi: 10.1109/72.182704 – volume-title: ICME 2000 year: 2000 ident: bibr5-095440503772680596 – ident: bibr2-095440503772680596 |
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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... 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... |
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| SubjectTerms | Applied sciences Classification Computer science; control theory; systems Control theory. Systems Deep drawing Exact sciences and technology Forming Geometry Industrial metrology. Testing Mechanical engineering. Machine design Metal forming Metals. Metallurgy Pattern recognition Process control. Computer integrated manufacturing Process controls Production techniques |
| Title | Using geometric shape variations to create an inverse model for a sheet metal process |
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