Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning

Tools

Okaro, Ikenna A. and Jayasinghe, Sarini and Sutcliffe, Chris and Black, Kate and Paoletti, Paolo and Green, Peter L. (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Additive Manufacturing, 27. pp. 42-53. ISSN 2214-8604 (https://doi.org/10.1016/j.addma.2019.01.006)

[thumbnail of Okaro-etal-AM2019-Automatic-fault-detection-laser-powder-bed-fusion-semi-supervised-machine-learning]
Preview
 Text. Filename: Okaro_etal_AM2019_Automatic_fault_detection_laser_powder_bed_fusion_semi_supervised_machine_learning.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still significant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certified and builds where the quality of the resulting components is unknown. This makes the approach cost efficient, particularly in scenarios where part certification is costly and time consuming. The study specifically analyses Laser Powder-Bed Fusion (L-PBF) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as ‘faulty’ or ‘acceptable’. Using a variety of approaches (Receiver Operating Characteristic (ROC) curves and 2-fold cross-validation), it is shown that, despite utilising a fraction of the available certification data, the semi-supervised approach can achieve results comparable to a benchmark case where all data points are labelled. The results show that semi-supervised learning is a promising approach for the automatic certification of AM builds that can be implemented at a fraction of the cost currently required.

ORCID iDs

Okaro, Ikenna A., Jayasinghe, Sarini ORCID logoORCID: https://orcid.org/0000-0003-4165-9496, Sutcliffe, Chris, Black, Kate, Paoletti, Paolo and Green, Peter L.;
  • Item type:Article
    ID code:85649
    Dates:
    Date
    Event
    1 May 2019
    Published
    11 February 2019
    Published Online
    23 January 2019
    Accepted
    14 August 2018
    Submitted
    Notes:Funding Information: This work was conducted in the feasibility study ‘Towards Additive Manufacturing Process Control using Semi-Supervised Machine Learning’ that was funded through the EPSRC Network Plus Grant: Industrial Systems in the Digital Age EP/P001246/1, https://connectedeverything.ac.uk/ . Publisher Copyright: © 2019 The Authors Ikenna A. Okaro, Sarini Jayasinghe, Chris Sutcliffe, Kate Black, Paolo Paoletti, Peter L. Green, Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning, Additive Manufacturing, Volume 27, 2019, Pages 42-53, https://doi.org/10.1016/j.addma.2019.01.006
    Subjects:Technology > Manufactures
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management > National Manufacturing Institute Scotland
    Depositing user: Pure Administrator
    Date deposited:01 Jun 2023 09:23
    Last modified:12 Dec 2024 14:43
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/85649
CORE (COnnecting REpositories)