Inspection of embedded internal features in additively manufactured metal parts using metrological x-ray computed tomography

Published

June 30, 2016

Author(s)

Felix H. Kim, Herminso Villarraga-Gomez, Shawn P. Moylan

Abstract

Advances in metal additive manufacturing (AM) have made it possible to build parts with complex geometry. Metal AM has a high potential to impact various industries including aerospace and automotive. Complex interior features can be designed to reduce weight and improve mechanical or thermal efficiency of the components. These features, however, are generally inaccessible from the outside to vision-based inspection techniques for quality control. Undesirable interior defects such as porosity and cracks can be formed due to sub-optimal processing parameters, poor feed stock material quality, or environmental effects [1-3]. The mission-critical structural components require thorough inspections for defects and dimensional accuracies. X-ray Computed Tomography (XCT) – based inspection is becoming a viable option for several manufacturing industries. XCT shows a clear three-dimensional (3D) internal structure of the part in inspection. XCT has been popularly used to understand materials structure and behavior [e.g., 4, 5]. As the technique is applied to industrial inspection settings, guidelines must be established prior to widespread adoption of the technique.

Proceedings Title

Proceedings of the American Society for Precision Engineering Summer Topical Meeting

Conference Dates

June 27-30, 2016

Conference Location

Raleigh, NC

Conference Title

2016 Summer Topical Meeting: Dimensional Accuracy and Surface Finish in Additive Manufacturing

Pub Type

Conferences

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Keywords

additive manufacturing

Citation

Kim, F. , Villarraga-Gomez, H. and Moylan, S. (2016), Inspection of embedded internal features in additively manufactured metal parts using metrological x-ray computed tomography, Proceedings of the American Society for Precision Engineering Summer Topical Meeting, Raleigh, NC, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=921026 (Accessed October 21, 2025)