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Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning

Liu, Mingyu; Senin, Nicola; Su, Rong; Leach, Richard

Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning Thumbnail


Authors

Mingyu Liu

Nicola Senin

Rong Su



Abstract

Quality monitoring for laser powder bed fusion (L-PBF), particularly in-process and real-time monitoring, is of importance for part quality assurance and manufacturing cost reduction. Measurement of layer surface topography is critical for quality monitoring, as any anomaly on layer surfaces can result in defects in the final part. In this paper, we propose a surface measurement method, based on the use of scattered light patterns and a convolutional autoencoder-based unsupervised machine learning method, designed and trained using a large set of scattering patterns simulated from reference surfaces using a scattering model. The advantage of using an autoencoder is that the monitoring model can be trained using solely data from acceptable surfaces, without the need to ensure the presence of representative observations for all the types of possible surface defects. The advantage of using simulated data for training is that we can obtain an effective monitoring solution without the need for a large collection of experimental observations. Here we report the results of a preliminary investigation on the performance of the proposed solution, where the trained autoencoder is tested on experimental data obtained off-process, using a dedicated experimental apparatus for generating and collecting light scattering patterns from manufactured L-PBF surfaces. Our results indicate that the proposed monitoring solution is capable of detecting both acceptable and anomalous surfaces. Although further validation is required to fully assess performance within an on-machine and in-process setup, our preliminary results are encouraging and provide a glimpse of the potential benefits of using our surface measurement solution for L-PBF in-process monitoring.

Citation

Liu, M., Senin, N., Su, R., & Leach, R. (2022). Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning. Measurement Science and Technology, 33(7), Article 074006. https://doi.org/10.1088/1361-6501/ac6569

Journal Article Type Article
Acceptance Date Apr 7, 2022
Online Publication Date Apr 21, 2022
Publication Date Jul 1, 2022
Deposit Date Jul 7, 2022
Publicly Available Date Jul 7, 2022
Journal Measurement Science and Technology
Print ISSN 0957-0233
Electronic ISSN 1361-6501
Publisher IOP Publishing
Peer Reviewed Peer Reviewed
Volume 33
Issue 7
Article Number 074006
DOI https://doi.org/10.1088/1361-6501/ac6569
Keywords Applied Mathematics; Instrumentation; Engineering (miscellaneous)
Public URL https://nottingham-repository.worktribe.com/output/7950517
Publisher URL https://iopscience.iop.org/article/10.1088/1361-6501/ac6569

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