Artificial Intelligence-Enhanced Mutli-Material Form Measurement for Additive Materials




Stavroulakis, P.
Davies, O.
Tzimiropoulos, G.
Leach, R.K.

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University of Texas at Austin


The range of materials used in additive manufacturing (AM) is ever growing nowadays. This puts pressure on post-process optical non-contact form measurement systems as different system architectures work most effectively with different types of materials and surface finishes. In this work, a data-driven artificial intelligence (AI) approach is used to recognise the material of a measured object and to fuse the measurements taken from three optical form measurement techniques to improve system performance compared to using each technique individually. More specifically, we present a form measurement system which uses AI and machine vision to enable the efficient combination of fringe projection, photogrammetry and deflectometry. The system has a target maximum permissible error of 50 μm and the prototype demonstrates the ability to measure complex geometries of AM objects, with a maximum size of (10 × 10 × 10) cm, with minimal user input.


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