Data registration for multi-method qualification of additive manufactured components
Maxwell R. Praniewicz, Gaurav Ameta, Jason Fox, Christopher Saldana
This work explores impact of refined surface registrations of voxel and point cloud data sets on accuracy of multi-method qualification of additively manufactured (AM) lattices. Voxel and point cloud sets of an AM lattice were aligned using derived geometry datums based on a theoretical supplemental surface definition, which has been established in recent draft standards, but has yet had limited examination using complex AM structures. A refined sampling registration approach for lattice geometry based on spatially-dependent subsampling is derived and shown to statistically decrease variation between measurement sources. This importance of well-defined sampling practice and definition is highlighted. The applicability of this approach for multi-method qualification of complex AM parts is discussed. This work lays the foundation of utilizing specifications under consideration in a new standard with possible verification techniques that can be employed.
, Ameta, G.
, Fox, J.
and Saldana, C.
Data registration for multi-method qualification of additive manufactured components, Additive Manufacturing, [online], https://doi.org/10.1016/j.addma.2020.101292, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928502
(Accessed June 24, 2022)