Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements

Jayasinghe, Sarini, Paoletti, Paolo ORCID: 0000-0001-6131-0377, Sutcliffe, Chris, Dardis, John, Jones, Nick and Green, Peter L (2022) Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. PROGRESS IN ADDITIVE MANUFACTURING, 7 (2). pp. 143-160.

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Official URL: http://dx.doi.org/10.20944/preprints202004.0055.v1

Abstract

<jats:p>This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three diﬀerent photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (&lt; 99% part density) and high density (&ge; 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantiﬁcation in machine-learnt predictions of L-PBF build quality.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Laser powder bed fusion, Automatic quality assessment, Machine learning, Uncertainty quantification
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	07 Oct 2021 15:30
Last Modified:	15 Mar 2024 06:50
DOI:	10.1007/s40964-021-00219-w
Open Access URL:	https://doi.org/10.1007/s40964-021-00219-w
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3139621