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Seeing Clearly: CT scanning, along with advanced analyses software, becoming key AM tool

Phillip Sperling
By Phillip Sperling Product manager for additive manufacturing, Volume Graphics

A conversation with Contributing Editor Kip Hanson

Kip Hanson: Phillip, most folks in the AM industry associate CT scanning and the resultant data analysis with metal powder bed-printed parts. What about polymers and composites? Is CT scanning needed there, as well?

Phillip Sperling, Volume Graphics: The increased use of CT scanning for metal powder bed fusion parts is usually associated with high-value parts and elevated quality requirements, many of which are produced using metal powder bed fusion. At the moment, we see increased requests for our solutions on parts made of engineering-grade polymers like PEEK, PEKK or ULTEM and for fiber-reinforced composites like Nylon 12 CF. This is also true for ceramic 3D printing. As with metal, we generally see requests for applications in these materials when the end user has high quality needs or regulations.

Phillip Sperling, Product manager for additive manufacturing, Volume Graphics

CT-scanning and image-analysis software gives manufacturers unprecedented opportunities for improvements to their 3D-printed part designs. Does the industry understand this, or is CT primarily used for metrology purposes?

I think that most manufacturers understand the value of CT analyses but often think of it in terms of answering a certain question like “Is there porosity” or using it for GD&T purposes. One big advantage of CT is answering a lot of questions in just one scan. For example, I can analyze my additively manufactured bracket for metrology purposes and, at the same time, look for porosity and inclusions and check for trapped powder—all using the same dataset.

Given Volume Graphics’ advanced modeling and analysis capabilities, do you foresee a time when manufacturers can avoid destructive testing entirely? If not, why?

Usually, all testing methods, whether destructive or non-destructive, deliver value in certain use cases. Compared with other methods, CT scanning is expensive and delivers significant amounts of information, so you would probably not use it where a simple metallographic image will do the job. For more advanced questions or where greater higher statistical analyses and volumetric inspection is necessary, sophisticated tools like CT make more sense.

CT scanning is necessary to validate 3D-printed part designs and the use of sound build parameters. But assuming an additive manufacturer has developed a stable process and has well-maintained equipment, do they still need routine CT scanning once parts have reached the production phase?

When AM users reach a high level of confidence in their production environment, they can use CT scanning to perform other tasks and answer additional questions. They might use it to analyze a witness specimen, for instance, or check single production parts to monitor quality statistics over time. We see that in non-AM industries, as well, where CT scanning is used extensively during the production ramp up to check all parts 100 percent and then later take samples to assure that the process remains in control.

Has the 3D-printing industry’s development of in-process metrology technology, such as melt pool monitoring and rigid environment controls, reduced the need for CT scanning?

At the moment, the opposite is happening. We see more and more in-process monitoring, and we get a lot of requests on how to use CT as a validation tool for such solutions. These technologies are at an early development stage and must be validated for every new part design and material.

At the same time, we see that in-process metrology solutions are very good at showing certain process defects like powder recoater issues. For other defects like micro porosity, these layer-based methods are limited, as defects can occur as a sum of several bad build layers, which is why CT scanning will be needed for some time. In the future, we can think of the collaborative use of monitoring solutions and CT to enable a more targeted NDT inspection, especially for bigger parts.

What are Volume Graphics and its parent company, Hexagon Manufacturing Intelligence, doing to increase interoperability of the various software packages used to design, prepare, build, and inspect entire 3D-printing process?

A big challenge for the AM user is the workflow through the different process steps from the design of the part, the build simulation, the support generation, build platform placement, and post processing.

A light-weight wheel carrier designed using MSC Apex Generative Design technology and analyzed with VGSTUDIO MAX. The visualization shows a color-coded nominal-actual comparison of the CT-scanned part with its CAD model.

At Hexagon, we can deliver solutions for most of the steps and we are working on a smooth workflow throughout our software products and file formats.

Similarly, CT scanning creates massive amounts of information. As its use becomes more widespread, what is Volume Graphics doing to streamline data management and make this information easier to use?

We are constantly working on improvements. Just to give you two examples: For data management, we already offer fully automatable analysis workflows. If users are scanning similar parts regularly, they can prepare macros and automatic reporting, and everything is done in our software. If the regulations of their industry allow, they can afterwards delete the CT raw data and just keep the most important information and images in a report. To centrally store CT results in quality-management or statistical-process-control software, you can now export results using the widely used Q-DAS data exchange format. To make everything easier to use, we are also constantly improving the user interface and user experience in our software. We’ve also added new features that follow industry standards and guidelines like the new P203 function to enable a standardized 3D-porosity analysis.

What’s next for CT scanning and Volume Graphics?

Some obvious advancements are higher resolution and more automation. However, the buzzword of the moment, artificial intelligence, is also a field that we are investigating very closely. Specifically, in the first stage, we are looking at defect detection using neural networks (NN). An NN trained with suitable data could reliably find and label defects in materials, even with lower-resolution data.

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