Yuxuan Wu, Sirish Namilae^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2067-2090, 2025, DOI:10.32604/cmes.2024.059091 - 27 January 2025

Abstract Powder bed fusion (PBF) in metallic additive manufacturing offers the ability to produce intricate geometries, high-strength components, and reliable products. However, powder processing before energy-based binding significantly impacts the final product’s integrity. Processing maps guide efficient process design to minimize defects, but creating them through experimentation alone is challenging due to the wide range of parameters, necessitating a comprehensive computational parametric analysis. In this study, we used the discrete element method to parametrically analyze the powder processing design space in PBF of stainless steel 316L powders. Uniform lattice parameter sweeps are often used for parametric… More >

Lu Liu¹, Bo Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012741

Abstract Powder-Bed Fusion (PBF) is a prominent metal additive manufacturing technology known for its adaptability and commercial viability. However, it is often hindered by defects such as voids, un-melted particles, microcracking, and columnar grains, which are generally more pronounced than those found in traditional manufacturing methods. Microcracking, in particular, poses a significant challenge, limiting the use of PBF materials in safety-critical applications across various industries. This study presents an advanced computational framework that effectively addresses the complex interactions of thermal, fluid dynamics, structural mechanics, crystallization, and fracture phenomena at meso and macroscopic levels. This framework has More >

Chenze Li¹, Manish Jain^1,2, Qian Liu¹, Zhuohan Cao¹, Michael Ferry³, Jamie J. Kruzic¹, Bernd Gludovatz¹, Xiaopeng Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011290

Abstract Laser powder bed fusion (LPBF) additive manufacturing (AM) technology has become a versatile tool for producing new microstructures in metal components, offering novel mechanical properties for different applications. In this work, enhanced ductility (~55% elongation) and tunable mechanical anisotropy (ratio of ductility along vertical to horizontal orientation from ~0.2 to ~1) were achieved for a CoCrNi medium entropy alloy (MEA) by multi-scale synergistic microstructure manipulation (i.e., melt pool boundary, grain morphology and crystallographic texture) through adjusting key LPBF processing parameters (e.g., laser power and scan speed). By increasing the volumetric energy density (VED) from 68.3… 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 >