Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality

optimize production

and reduce costs. However

it faces the difficulty caused by limited labeled samples

for which co-training based semi-supervised learning offers a potential solution. So in this paper

a novel soft sensing method for mechanical properties based on improved co-training (ICO) is proposed. Compared with the existing co-training framework

the proposed ICO introduces improvements from the aspects of multiple view partition

confidence estimation

and pseudo-label assignment. Specifically

(ⅰ) in the stage of multiple view partition

ICO integrates metallurgical mechanisms of hot rolling processes and statistical mutual information to achieve a balance between view sufficiency and independence

which improves model performance and interpretability; (ⅱ) in the stage of confidence estimation

ICO evaluates the confidence of unlabeled samples at the cluster level rather than at the level of a single sample

which facilitates the exploration of sample distribution and the selection of representative samples; (ⅲ) in the pseudo-label assignment stage

ICO adopts a safe pseudo-label algorithm (which is called SAFER by its author and originally used for each single sample) to assign pseudo-labels for cluster of samples with the highest confidence determined in the previous step stage

to take advantage of the merit of handling unlabeled samples at the cluster leve

l mentioned above on one hand

and the merit of SAFER in enhancing the quality of pseudo-labels on the other hand. The proposed soft sensing method effectively predicts mechanical properties on the real hot rolling dataset

achieving approximately 5% improvement in

R

2

compared to traditional supervised learning.