Abstract
The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.
Highlights
The powder bed fusion (PBF) additive manufacturing process uses an electron or laser beam to fuse metallic powders over a build platform to print one layer of the build dictated by a computer-aided design (CAD) software
Other papers [17,18] have tried to investigate defects from a different perspective. These papers mainly focused on the formation of defects, such as the lack of fusion, porosity, surface roughness, etc., on the build direction. An analysis of these defects along the bead cross section based on process parameters such as laser power and scan speed is largely missing from the literature
An analysis of variance (ANOVA) was used to detect if the process variables such as laser power and scan speed have any significant impact on the bead cross section, such as contact angle, porosity and melt pool size
Summary
The powder bed fusion (PBF) additive manufacturing process uses an electron or laser beam to fuse metallic powders over a build platform to print one layer of the build dictated by a computer-aided design (CAD) software. Researchers have tried to analyze the characteristics of different aspects of the build to optimize the parameters that can influence the process Geometrical features such as contact angle between the present and the preceding layers, which dictates the wetting behavior of the melt pool, have been studied by several scientists. These papers mainly focused on the formation of defects, such as the lack of fusion, porosity, surface roughness, etc., on the build direction An analysis of these defects along the bead cross section based on process parameters such as laser power and scan speed is largely missing from the literature. Casalino et al [30] investigated the impact of laser power and scan speed on mechanical properties, such as hardness and tensile strength of the final build They found out that increasing energy density decreases surface roughness and increases hardness. A multi-response analysis is conducted in this work, including all the features as a part of process parameter optimization
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