A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Peng:2022:DDCLS,

• author = "Chong Peng and Zhijian Cheng and Hongru Ren and Renquan Lu",
• booktitle = "2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)",
• title = "A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming",
• year = "2022",
• pages = "77--81",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/DDCLS55054.2022.9858371",
• ISSN = "2767-9861",
• month = aug,
• notes = "Also known as \cite{9858371}",

}

Genetic Programming entries for Chong Peng Zhijian Cheng Hongru Ren Renquan Lu

Citations