X-ray computed tomography, also known as CT or CAT scan, has been used for years in the medical field. More recently, manufacturers have begun using it to measure a part’s geometrical dimensions, including both internal and external features. It is frequently used in additive manufacturing to non-destructively measure complex parts. In a thesis entitled “Studies of Dimensional Metrology with X-ray CAT scan,” a University of North Carolina at Charlotte student named Herminso Villarraga Gómez conducts several experiments that evaluate the performance of cone-beam CT measurements and their uncertainty estimates, comparing them to reference measurements mostly obtained from tactile coordinate measurement machines (CMMs).
Gómez points out that the field of CT metrology still faces challenges in trying to estimate measurement uncertainties, “mainly due to the plethora of influencing factors contributing to the CT measurement process.” His thesis attempts to further understand the role of variables affecting the precision and accuracy of CT dimensional measurements. The main CT variables he investigates are temperature in the X–ray CT enclosure, number of projections for a CT scan, workpiece tilt orientation, sample image magnification, material thickness influences, software post–filtration, threshold determination, and measurement strategies.
In some experiments, Gómez contrasted the results against simulations performed in Matlab software and another simulation tool called Dreamcaster.
“For dimensions of geometric features ranging from 0.5 mm to 65 mm, a comparison between dimensional CT and CMM measurements, performed at optimized conditions, typically resulted in differences of approximately 5 µm or less for data associated with dimensional lengths(length, width, height, and diameters) and around 5 to 50 µm for data associated with measurements of form, while expanded uncertainties computed for the CT measurements ranged from 1 to over 50 µm,” he states.
He also assessed methods for estimating measurement uncertainty of CT scanning. He presents a thorough study of metrics used for proficiency testing, including tests of statistical consistency (null-hypothesis testing) performed with Monte Carlo simulation, and applies them to results from two recent CT interlaboratory comparisons.
“In particular, it is shown that the use of the En-metric in the current state of CT interlaboratory comparisons could be difficult to interpret when used to evaluate performance and/or statistical consistency of CT measurement sets,” he continues.
Gómez’s study is important when applied to additive manufacturing because, particularly in fields such as aerospace, precision of parts is of the utmost importance. A flawless method of measurements must be established, and CT scanning has great potential, and is indeed already in widespread use. But it’s not perfect, as Gómez points out, and his thesis attempts to assess and better understand its limitations.
One of the benefits of additive manufacturing is that it can create parts with complex geometries, both internal and external, and those can be difficult to measure, especially non-destructively. QA for 3D printing is still problematic for many parts. CT scanning is a largely reliable method for measuring the complicated internal channels and features that additive manufacturing is known for, and Gómez’s work is a big step toward making it even more reliable.