Investigation of processing windows in additive manufacturing of AlSi10Mg for faster production utilizing data-driven modeling
Description
To reduce production time and decrease production cost, the increase of layer thickness is an adequate option in powder bed fusion. To investigate the influence of high layer thicknesses in the production of parts, a wide range of process parameters must be considered since appropriate processing windows are expected to be very narrow. Therefore, exploring the design space solely by the use of experimental investigations would be inefficient. A more efficient way is to use experiments in combination with a mathematical modeling approach.
Such an approach was made in the study described in https://doi.org/10.1016/j.addma.2022.102858. In order to determine the relationships between process parameters in laser powder bed fusion (PBF-LB/M) and final porosity in AlSi10Mg, samples were processed following a space-filling experimental design using a standard industrial PBF-LB/M system equipped with a 400 W laser. A total of 144 samples were fabricated considering layer thicknesses of 30 μm, 45 μm, 60 μm, and 90 μm. Afterwards, porosity was assessed using image analysis and computed tomography. Different types of defects were found as expected, however, fully dense parts were realized in case of every considered layer thickness. Predictive models were developed using data-driven approaches, eventually enabling multivariate analysis of the correlations and determination of appropriate processing conditions resulting in both low porosity of parts and high build rates.
This dataset provides the process parameter combinations and the porosity values which were used to develop the predictive models using data-driven approaches. Moreover, meta data about the experimental design, the sample manufacturing, the image capturing, and the porosity determination are given.
IMPORTANT: In case you use the data please cite our corresponding article https://doi.org/10.1016/j.addma.2022.102858
License
Except where otherwised noted, this item's license is described as Creative Commons Attribution 4.0