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Methods for Rapid Pore Classification in Metal Additive Manufacturing

Snell, R, Tammas-Williams, S, Chechik, L, Lyle, A, Hernández-Nava, E, Boig, C, Panoutsos, G and Todd, I (2019) Methods for Rapid Pore Classification in Metal Additive Manufacturing. JOM Journal of the Minerals, Metals and Materials Society. ISSN 1047-4838

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Abstract

The additive manufacturing of metals requires optimisation to find the melting conditions that give the desired material properties. A key aspect of the optimisation is minimising the porosity that forms during the melting process. A corresponding analysis of pores of different types (e.g. lack of fusion or keyholes) is therefore desirable. Knowing that pores form under different thermal conditions allows greater insight into the optimisation process. In this work, two pore classification methods were trialled: unsupervised machine learning and defined limits. These methods were applied to 3D pore data from X-ray computed tomography and 2D pore data from micrographs. Data were collected from multiple alloys (Ti-6Al-4V, Inconel 718, Ti-5553 and Haynes 282). Machine learning was found to be the most useful for 3D pore data and defined limits for the 2D pore data; the latter worked by optimising the limits using energy densities.

Item Type: Article
Uncontrolled Keywords: 0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0912 Materials Engineering
Subjects: T Technology > TN Mining engineering. Metallurgy
Divisions: Maritime & Mechanical Engineering
Publisher: Springer Verlag
Date Deposited: 10 Sep 2019 09:40
Last Modified: 10 Sep 2019 09:45
DOI or Identification number: 10.1007/s11837-019-03761-9
URI: http://researchonline.ljmu.ac.uk/id/eprint/11316

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