Xiayun Zhao^1,*, Haolin Zhang¹, Md Jahangir Alam¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011479

Abstract Laser powder bed fusion (LPBF) is one predominant additive manufacturing (AM) technology for producing metallic parts with sophisticated designs that can find numerous applications in critical industries such as aerospace. To achieve precise, resilient, and intelligent LPBF, a comprehensive understanding of the dynamic processes and material responses within the actual conditions of LPBF-based AM is essential. However, obtaining such insights is challenging due to the intricate interactions among the laser, powder, part layers, and gas flow, among other factors. Multimodal in-situ monitoring is desired to visualize diverse process signatures, allowing for the direct and thorough… More >

Xin Lin^1,2,3, Kunpeng Zhu^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012499

Abstract This paper reveals the interplay mechanism between melt pool, spattering and vapors, aiming to further improve the forming quality through in-situ monitoring with a CMOS camera. A Residual Network based on Convolutional Block Attention Module and Focal loss function is proposed to extract multi-scale features of single tracks and learn about their behavior changes. A t-SNE clustering analysis is utilized to analysis a large amount of time sequence data on the melt pool by collecting the schlieren photographs. It is found that patterns of unstable melt pool changing corelate to the defects in single tracks, More >