journal article·2025-02-01

Validation of spatial accuracy with high-speed imaging to support in situ flaw detection for laser directed energy deposition processes

Dieter De Baere, Jorge Sanchez-Medina, Charles Snyers, Sukayna Fakher, Zoé Jardon, Michaël Hinderdael, Jan Helsen

Journal of Laser Applications· Volume 37· Issue 1
Publisher page

Abstract

The application of in situ monitoring systems for part quality verification or process qualification of the laser based directed energy deposition process will require the ability to confidently detect (or estimate) the type, size, and location of defects in an additive manufactured component. These developments are requested by the industry to supplement the expensive and time-consuming ex situ inspections for part certification to further support the number of viable business cases. This confidence level of the in situ monitoring solutions should be comparable to that of the ex situ inspection techniques (such as microcomputed tomography). These developments will also require very large experimental data sets with spatially resolved in situ monitoring signals which must be correlated to ex situ inspection data. A crucial element in this correlation is the spatial absolute accuracy of these signals from both in situ and ex situ solutions. The in situ solutions should, preferably, at least match with ex situ accuracy. Currently, there is no consensus framework for validation of the spatial accuracy of the in situ monitoring signals. Therefore, a method will be presented in this paper to evaluate the spatial accuracy obtainable with the typical available location information on ethernet field bus implemented in the laser based directed energy deposition process. The accuracy is verified by the implementation of a high-speed imaging setup that additionally enables to verify the synchronization accuracy of the imaging solutions. Cameras act often as a source of data for the in situ monitoring solutions of the melt pool.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Vrije Universiteit Brussel, Acoustics and Vibration Research Group , Pleinlaan 2, Brussels B-1050,

Funding

Fonds Wetenschappelijk Onderzoek

References (14)

Show source-provided references
1. Laser additive manufacturing processes for near net shape components10.1007/978-3-030-10579-2_5 ↗
2. Additive manufacturing materials, methods and applications: A review10.1016/j.matpr.2021.04.379 ↗
3. Additive manufacturing methods: Techniques, materials, and closed-loop control applications10.1007/s00170-020-05663-6 ↗
4. Advances and researches on non destructive testing: A review10.1016/j.matpr.2017.11.620 ↗
5. Progress towards metal additive manufacturing standardization to support qualification and certification10.1007/s11837-017-2265-2 ↗
6. Optimizing quality inspection and control in powder bed metal additive manufacturing: Challenges and research directions10.1109/JPROC.2021.3054628 ↗
7. Spectroscopic monitoring and melt pool temperature estimation during the laser metal deposition process10.2351/1.4943995 ↗
8. A review on process monitoring and control in metal-based additive manufacturing10.1115/1.4028540 ↗
9. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing10.1016/j.matdes.2016.01.099 ↗
11. Initial work on the characterization of additive manufacturing (3D printing) using software image analysis10.3390/machines3020055 ↗
12. Lukas Budach , MoritzFeuerpfeil, NinaIhde, AndreaNathansen, NeleNoack, HendrikPatzlaff, FelixNaumann, and HazarHarmouch, “The effects of data quality on machine learning performance,” arXiv:2207.14529 (2022).
13. Confident learning: Estimating uncertainty in dataset labels10.1613/jair.1.12125 ↗
14. AprilTag: A robust and flexible visual fiducial system