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Structural Steel Products

Structural Steel Products

Structural steel products are created within a structural mill. The process begins with a beam blank that is reheated and then fed through a series of rollers. These rollers progressively shape the metal into its final form. Adjusting the spacings between the rollers allows for the production of different sections with the same nominal dimensions.
Once shaped, the steel's final form emerges as a continuous length, which is then segmented by a hot saw into manageable pieces. These segments are...

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Cross-Sectional Cladding Segmentation of Stainless-Steel/Carbon-Steel Clad Wire Rods Using an Improved U-Net with

Lei Zeng^1,2, Zecheng Zhuang^1,2, Geng Zhou³

¹School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Materials (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces an enhanced U-Net model for precise cladding segmentation in wire rods, improving quality assessment. The advanced framework achieves superior accuracy in identifying cladding boundaries and thickness for industrial applications.

Keywords:

attention mechanism bridge cable quality inspection cladding segmentation improved U-Net multi-scale feature fusion stainless-steel/carbon-steel clad wire rod

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Area of Science:

Materials Science
Computer Vision
Image Processing

Background:

Accurate cladding segmentation is critical for quality control of stainless-steel/carbon-steel clad wire rods.
Challenges include weak core-cladding contrast, narrow transition zones, and thickness variations.

Purpose of the Study:

To develop an improved U-Net framework for robust cladding identification in clad wire rods.
To enhance the accuracy and reliability of quantitative quality assessment.

Main Methods:

Proposed an improved U-Net framework integrating ResNet50, atrous spatial pyramid pooling, and a convolutional block attention module.
Constructed a dataset of 18,566 cross-sectional images with pixel-level annotation.
Utilized standardized specimen preparation, chemical color development, and data augmentation.

Main Results:

Achieved high performance metrics: 97.29% mean pixel accuracy, 88.82% cladding intersection over union, and 93.72% mean intersection over union.
Outperformed baseline U-Net by significant margins in key segmentation metrics.
Demonstrated improved boundary continuity, detail preservation, and segmentation stability over other CNN models.

Conclusions:

The proposed framework offers a practical and reliable vision-based solution for cladding segmentation.
Enables accurate cladding thickness measurement, eccentricity evaluation, and uniformity assessment.
Facilitates efficient batch quality inspection of clad wire rods.