Manufacturing modeling using an evolutionary fuzzy regression
Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can on...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/30918 |
| _version_ | 1848753228742656000 |
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| author | Chan, Kit Yan Ling, S. Dillon, Tharam Kwong, C. |
| author2 | Chin-Teng Lin |
| author_facet | Chin-Teng Lin Chan, Kit Yan Ling, S. Dillon, Tharam Kwong, C. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out. |
| first_indexed | 2025-11-14T08:21:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-30918 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:21:11Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-309182023-01-27T05:52:08Z Manufacturing modeling using an evolutionary fuzzy regression Chan, Kit Yan Ling, S. Dillon, Tharam Kwong, C. Chin-Teng Lin Yau-Huang Kuo manufacturing process modelling fuzzy regression Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out. 2011 Conference Paper http://hdl.handle.net/20.500.11937/30918 10.1109/FUZZY.2011.6007322 IEEE restricted |
| spellingShingle | manufacturing process modelling fuzzy regression Chan, Kit Yan Ling, S. Dillon, Tharam Kwong, C. Manufacturing modeling using an evolutionary fuzzy regression |
| title | Manufacturing modeling using an evolutionary fuzzy regression |
| title_full | Manufacturing modeling using an evolutionary fuzzy regression |
| title_fullStr | Manufacturing modeling using an evolutionary fuzzy regression |
| title_full_unstemmed | Manufacturing modeling using an evolutionary fuzzy regression |
| title_short | Manufacturing modeling using an evolutionary fuzzy regression |
| title_sort | manufacturing modeling using an evolutionary fuzzy regression |
| topic | manufacturing process modelling fuzzy regression |
| url | http://hdl.handle.net/20.500.11937/30918 |