Surface topographies from electron optical images in electron beam powder bed fusion for process monitoring and control

Renner J, Breuning C, Markl M, Körner C (2022)


Publication Language: English

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 60

Article Number: 103172

DOI: 10.1016/j.addma.2022.103172

Abstract

Electron optical imaging is a process monitoring method in electron beam powder bed fusion, which enables porosity detection in situ for every layer of a part. The two main defect types occurring in a build process are porosity, due to insufficient local energy input, and surface bulging, when it is too high. This work adds the ability to measure surface topographies quantitatively and in situ to electron optical imaging. To this end, electron optical images are recorded with a four detector system from melt surfaces fabricated on a Ti–6Al–4V plate. A complete computation chain is proposed, based on a developed model of the imaging process. The model explains the correction of image distortions and the calibrated calculation of the gradient information of the respective surfaces. It becomes possible to reconstruct height maps of melt surfaces by the usage of a suitable normal integration algorithm. The computation chain is validated by the comparison of the resulting reconstructions with laser scanning microscope measurements. Electron optical imaging is able to measure porosity and bulging simultaneously, which allows to gain important process information for process control and the future implementation of feedback control loops.

Authors with CRIS profile

How to cite

APA:

Renner, J., Breuning, C., Markl, M., & Körner, C. (2022). Surface topographies from electron optical images in electron beam powder bed fusion for process monitoring and control. Additive Manufacturing, 60. https://doi.org/10.1016/j.addma.2022.103172

MLA:

Renner, Jakob, et al. "Surface topographies from electron optical images in electron beam powder bed fusion for process monitoring and control." Additive Manufacturing 60 (2022).

BibTeX: Download