Vamsi Subraveti, Brodan Richter, Saikumar R. Yeratapally, Caglar Oskay
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引用次数: 0
Abstract
Powder bed fusion (PBF) is an additive manufacturing technique that has experienced widespread growth in recent years due to various process advantages. However, defects such as porosity and the effects that porosity have on the mechanical performance remain a concern for parts manufactured using PBF. This work develops a three-dimensional framework to simulate lack-of-fusion (LoF) porosity during powder bed fusion using the voxel-based lack-of-fusion model. The framework is calibrated and validated against previously reported LoF porosity measurements and maximum equivalent pore diameter. The framework is used to study the influence of laser power, velocity, hatch spacing, and layer thickness on porosity volume fraction and morphology. Power and velocity have a linear relationship to porosity, and power has a stronger effect than velocity on changing porosity. This stronger effect of power versus velocity contributes to high variability when relating energy density to porosity, and a modified energy density metric that weighs power heavier is shown to reduce variability. In contrast to power and velocity, hatch spacing and layer thickness have a more complicated relationship with porosity, especially at their extrema. The influence of hatch spacing and layer thickness on pore equivalent diameter and sphericity is also explored, and four distinct morphological regimes are characterized. A LoF criteria proposed in a previous work are also confirmed. Overall, the framework offers a methodology to simulate porosity quantity and morphology and interfaces with other process–structure–property prediction techniques to support the design and development of reduced-defect powder bed fusion parts.
期刊介绍:
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.