An ensemble JITL method based on multi-weighted similarity measures for cold rolling force prediction. (July 2022)

• Record Type: Journal Article
• Authors: Wei, Lixin; Zhai, Bohao; Sun, Hao; Hu, Ziyu; Zhao, Zhiwei
• Is Part Of: ISA transactions. Volume 126(2022)
• Page Start: 326
• Page End: 337
• Publication Date: 2022-07
• Subjects: Cold tandem mill -- Rolling force prediction -- Ensemble learning -- Just-in-time-learning -- Weighted similarity measure; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.07.030
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In the cold tandem rolling process, the product quality and yield are affected by the accuracy of rolling force prediction directly. Fix prediction model is not applicable to the multi-operating conditions rolling environment. In addition, appropriate samples can be hardly selected by a single similarity measure because of the insufficient process knowledge. In order to solve these issues, an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures (MWS-EJITL) is proposed. Firstly, multi-weighted similarity measures is used to select relevant samples. Then, the local model is constructed and the output value of the query data is estimated. Finally, the ensemble learning strategy is adopted to integrate the outputs of each local model. On this basis, the cumulative similarity factor is introduced to optimize the number of samples of local modeling, and the similarity threshold is set to update the local model adaptively. The rolling force prediction experiment verify the effectiveness and accuracy of MWS-EJITL method. Highlights: A rolling force prediction model based on MWS-EJITL is established to deal with the multi-operating conditions. Three weighted similarity measures based on correlation are defined to select appropriate local modeling samples. Cumulative similarity factor and similarity threshold are set to enhance the adaptive capability.