Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Version 1 : Received: 17 September 2018 / Approved: 18 September 2018 / Online: 18 September 2018 (11:21:58 CEST)

A peer-reviewed article of this Preprint also exists.

Okaro, I.A.; Jayasinghe, S.; Sutcliffe, C.; Black, K.; Paoletti, P.; Green, P.L. Automatic Fault Detection for Laser Powder-Bed Fusion using Semi-Supervised Machine Learning. Additive Manufacturing 2019 Okaro, I.A.; Jayasinghe, S.; Sutcliffe, C.; Black, K.; Paoletti, P.; Green, P.L. Automatic Fault Detection for Laser Powder-Bed Fusion using Semi-Supervised Machine Learning. Additive Manufacturing 2019

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 signi cant 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 certi ed and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certi cation is costly and time consuming. The study speci cally analyses Selective Laser Melting (SLM) 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'. A fully supervised approach identi ed faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certi cation data. The results show that semi-supervised learning is a promising approach for the automatic certi cation of AM builds that can be implemented at a fraction of the cost currently required.

Subject Areas

SLM, Process Control, Semi-supervised Machine Learning, Randomised Singular Value Decomposition

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