Methods for Rapid Pore Classification in Metal Additive Manufacturing
By Robert Snell, S. Tammas-Williams, L. Chechik, Alistair Lyle, E. Hernández-Nava, C. Boig, G. Panoutsos, Iain Todd. JOM, 2019
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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.
Subfields: Automotive Engineering, Materials Chemistry, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/023ba436af1dadc01951b0f496d0d4945fe82079
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In Materials Chemistry
Citations in the corpus
- In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review (2021, background): “variables such as build time [143] or part properties such as porosity [145].”