A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Peng:2022:DDCLS,
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author = "Chong Peng and Zhijian Cheng and Hongru Ren and
Renquan Lu",
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booktitle = "2022 IEEE 11th Data Driven Control and Learning
Systems Conference (DDCLS)",
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title = "A Quality Prediction Hybrid Model of Manufacturing
Process Based on Genetic Programming",
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year = "2022",
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pages = "77--81",
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abstract = "The design of manufacturing parameters in the initial
stage is backed by quality prediction to realise
intelligent manufacturing. Accurate prediction
translates to better quality, lower costs and more
flexibility. However, the real production is a
complicated and variable process, most of which
involved multiple parameters simultaneously. The data
on the basis of feature construction can filter the
impurities of data, accuracy of predictive model can be
satisfied. Existing approaches to provide results are
useless when the insufficient mining of the
relationship between the data or the some case without
adequate manufacturing data and expertise. In this
paper, a two-stage hybrid approach with genetic
programming is proposed for quality prediction. The
feature construction is realized by genetic programming
in the first stage, and the new features are used as
additives to subsequent stage of the extreme gradient
boosting. The comparison experiments indicate that the
two-stage hybrid model outperforms the existing methods
in overall performance.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/DDCLS55054.2022.9858371",
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ISSN = "2767-9861",
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month = aug,
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notes = "Also known as \cite{9858371}",
- }
Genetic Programming entries for
Chong Peng
Zhijian Cheng
Hongru Ren
Renquan Lu
Citations