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The purpose of this paper is to examine the macroscopic and microscopic fields of fiber suspensions in the non‐isothermal situations, also to examine the effect of fiber on this non‐isothermal system.

Control equations are coupled and simultaneously solved by collocated finite volume method on fully triangular meshes.

Temperature dependence and wall temperature have significant effect on both macroscopic and microscopic fields of fiber suspensions. Moreover, the influence of fiber on the non‐isothermal system is similar to that of the isothermal system.

This is the first time that the microstructures of both molecules and fibers are presented in the non‐isothermal condition and it is hoped that the results will provide more insight into the microscopics of complex flows.

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.