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