Data registration for multi-method qualification of additive manufactured components

Area of Science:

• Metrology and Additive Manufacturing
• Surface Registration Techniques
• Quality Assurance for Complex Geometries

Background:

• Additive Manufacturing (AM) presents challenges for metrological qualification due to complex geometries.
• Existing surface registration methods require refinement for accurate multi-method qualification of AM lattices.
• Draft standards propose supplemental surface definitions, but their application to complex AM structures needs examination.

Purpose of the Study:

• To refine surface registration methods for metrological datasets of additively manufactured lattices.
• To improve the accuracy and reduce variation in multi-method qualification of AM parts.
• To investigate the application of theoretical supplemental surface definitions in complex AM structures.

Main Methods:

• Alignment of X-ray computed tomography and coordinate measurement machine datasets using derived geometry datum features.
• Development of a refined, spatially-dependent subsampling approach for lattice geometry registration.
• Statistical analysis to assess the variation reduction between different measurement sources.

Main Results:

• The refined sampling registration approach statistically decreases variation between measurement sources.
• The derived geometry datum features, based on supplemental surface definitions, facilitate accurate alignment.
• Demonstrated reduction in variability for multi-method qualification of additively manufactured lattices.

Conclusions:

• A refined sampling registration method enhances the multi-method qualification accuracy of additively manufactured lattices.
• Well-defined sampling practices and theoretical supplemental surface definitions are crucial for complex AM part qualification.
• This work provides a foundation for utilizing new standard specifications and verification techniques in AM metrology.