By Dr. Daniela Handl, General Manager, Volume Graphics, Hexagon
AM has reached a level of acceptance and maturity that some may find surprising: In 2025, GE Aviation will use additively manufactured turbine engine blades to power its GE9X, the “world’s largest jet engine.”
While providers of AM continue to refine their systems and technologies to capture further breakthroughs in performance and cost, GE’s choice is a sure sign that AM is here for the long run. Considering the vital role of quality in securing certification for aircraft flight, you can be certain those turbine blades will be inspected at the highest—and deepest—levels.
One important tool supporting turbine blade research, design, and first-article and final inspection is industrial CT scanning and data analysis. Already playing a significant role in the automotive industry and beyond, the technology is complementing AM in powerful ways.
No matter what type of AM system—fusion or deposition—or mix of materials, be they titanium alloys, tool steel, aluminum, copper, plastics, or fiber composites, CT scanning is a highly accurate way to non-destructively test AM parts.
First, it can see almost layer-by-layer to provide fundamental checks on porosity, density, distortions, fractures, and tolerances of finished parts. These can be measured against outside industry standards to ensure basic commercial compliance.
But a CT scan also captures a vast quantity of other valuable data that provide deep insights into the 3D-printing process itself: wisdom that can be used to query and adjust part design, modify build-chamber setup, or adjust variables in process activity such as meltpool dimensions and temperatures.
Meltpool monitoring for Laser Powder Bed Fusion (LPBF), for example, collects significant amounts of image data that can be aligned and compared to the analysis and visualization information from a subsequent CT scan. This enables engineers to virtually slice through a print and review the image stack for variations the camera alone cannot reveal. How do imperfections match with the meltpool data? Can I correlate this somehow to my process? Are the structures too thin or too thick? Might variation stem from the recoater blade, or build orientation? Is there too much porosity—and is that a result of the gas environment or the powder quality? Or does this comparison validate why my current approach is right?
Valuable wisdom is gained through such queries. These insights help users with the next build and even with the design process. Advanced practitioners of CT analysis are using it to inform design and simulation feedback loops in workflows that can extend from first-article inspection through to final production—and even beyond to product lifecycle considerations.
Industry leaders employing AM are embarking on a journey to connect and enhance the digital product-development path in ways that save time, effort, and total resources. A data-rich CT scan holds information not only about metrology, but also on tolerances and assembly fit. Such knowledge passes to all disciplines and can be collected for statistical use in CAD and downstream to factory floor Manufacturing Execution Systems (MES).
Collaboration is underway to harness AI and deep learning to further support the role CT-scan data analysis plays in design and inspection. This will lead to even more profound levels of wisdom and insight that will serve R&D, inspection, in-line evaluation and certification in both advanced AM and traditional manufacturing disciplines offers the depth, breath and level of automation required for advancing new and evolving AM technologies.