Automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network. (March 2021)

• Record Type: Journal Article
• Authors: He, Xiang; Wang, Tianqi; Wu, Kaixuan; Liu, Haihua
• Is Part Of: Measurement. Volume 173(2021)
• Publication Date: 2021-03
• Subjects: Wire arc additive manufacturing -- Magneto-optical imaging -- Image enhancement -- Convolutional neural network -- Defect classification; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108633
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A MOI technique provides a novel and visual WAAM product quality inspection method. The characteristics of the magneto-optical image of WAAM product is analyzed. Remanence/magneto-optical imaging is improved by setting light intensity thresholds. The optimal light intensity thresholds for MO image enhancement are discussed. A CSCNN model provides highly precise classification results based on image dataset. Abstract: Wire arc additive metal manufacturing (WAAM) is one of the most revolutionary and popular manufacturing processes. However, the poor quality is an important factor restricting the development of this technology. In particular, it is difficult to detect the small defects on the surface and subsurface of the manufactured products. To cope with this issue, we propose a new method for automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network. The improved remanence/magneto-optical imaging is used to obtain clear magneto-optical images. A convolutional neural network model is then deployed to detect the defects in magneto-optical images. The proposed method is effective in automatic detection of the surface defects of low-carbon steel WAAM products.