Laser-powder bed fusion (LPBF) is increasingly used as a successful metal additive manufacturing technology in different industrial sectors, such as biomedical and aerospace, especially for the production of parts with high geometrical complexity. However, LPBF is still affected by repeatability issues and the presence of several defects within the fabricated components. This aspect is therefore a hindrance in the spreading of this prospective technology, which in several cases is still not able to meet the stringent quality requirements imposed by such industries. In order to overcome these issues, X-ray computed tomography (CT) acquires a fundamental role in the understanding of critical aspects and for process development and optimization. Also considering the growing interest regarding the development of LPBF process monitoring systems, which seek to identify and possibly correct defects during fabrication, CT is very much needed for the generation of reference data concerning actual geometry and internal defects. Nevertheless, this implies the need for an accurate comparison between datasets obtained by in-process acquisitions and post-process measurements, with the aim of successfully correlating process events to actual defects. However, to enhance the comparison accuracy, deformations occurring during and after manufacturing need to be considered when performing in- and post-process data registration, given that this aspect is greatly affecting the outcome of correlations. This work studies a sample geometry that is specifically developed for enhancing data registration accuracy, along with the related alignment procedure. The methodology has also the advantage to be applicable to any kind of in-process monitoring data, despite this paper uses optical imaging for the acquisition of long-exposure digital images, which proved to be suited for detecting spatter particles. Post-process datasets were obtained through metrological X-ray computed tomography, which provides detailed information regarding internal defects.

On the use of X-ray computed tomography for the improvement of metal laser powder bed fusion process monitoring

Filippo Zanini;Simone Carmignato
2023

Abstract

Laser-powder bed fusion (LPBF) is increasingly used as a successful metal additive manufacturing technology in different industrial sectors, such as biomedical and aerospace, especially for the production of parts with high geometrical complexity. However, LPBF is still affected by repeatability issues and the presence of several defects within the fabricated components. This aspect is therefore a hindrance in the spreading of this prospective technology, which in several cases is still not able to meet the stringent quality requirements imposed by such industries. In order to overcome these issues, X-ray computed tomography (CT) acquires a fundamental role in the understanding of critical aspects and for process development and optimization. Also considering the growing interest regarding the development of LPBF process monitoring systems, which seek to identify and possibly correct defects during fabrication, CT is very much needed for the generation of reference data concerning actual geometry and internal defects. Nevertheless, this implies the need for an accurate comparison between datasets obtained by in-process acquisitions and post-process measurements, with the aim of successfully correlating process events to actual defects. However, to enhance the comparison accuracy, deformations occurring during and after manufacturing need to be considered when performing in- and post-process data registration, given that this aspect is greatly affecting the outcome of correlations. This work studies a sample geometry that is specifically developed for enhancing data registration accuracy, along with the related alignment procedure. The methodology has also the advantage to be applicable to any kind of in-process monitoring data, despite this paper uses optical imaging for the acquisition of long-exposure digital images, which proved to be suited for detecting spatter particles. Post-process datasets were obtained through metrological X-ray computed tomography, which provides detailed information regarding internal defects.
2023
Proceedings of International Conference on Industrial Computed Tomography - iCT 2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3500322
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