An ensemble JITL method based on multi-weighted similarity measures for cold rolling force prediction

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. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

In the cold tandem rolling process, the accuracy of rolling force prediction directly affects product quality and yield. This study proposes an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures to address the limitations of fixed prediction models and single similarity measures.