Additive manufacturing is an inherently digital process, and if you ask Isabelle Hachette, CEO at Interspectral, a Swedish technology company specialising in 3D visualisation and data fusion, ‘there is no lack of data’ from AM – just a lack of tools to fully utilise it.
For TCT Magazine's annual focus on manufacturing quality technologies, we're taking a look at the growing importance of quality and inspection solutions for AM by speaking to the companies leading the charge to provide greater visibility and assurance for AM processes.
Here, Hachette discusses why now, more than ever, quality assurance technologies are playing a crucial role as AM continues its path to production.
TCT: Tell us about Interspectral, where did it all start?
IH: We are actually entering from a neighboring industry, scientific visualization. We entered the additive manufacturing industry because we saw that there was a high demand for our technology, specifically to enable serial production, and [through] this research project and the industrial cooperation that we had in Sweden, we discovered that there was a lot of data that was measured and acquired in the production process. We also discovered that there was no standardized way of handling the data in an efficient way, and many times the data could consist of several terabytes and that made it difficult to use the technology in a larger scale. So we think that we can take a lead in developing the market to efficiently read and visualize and draw conclusions [from] this big data.
TCT: What is Interspectral bringing to the market that’s different to current quality assurance and process monitoring solutions on the market today?
IH: We provide a unique value to quality assurance and process monitoring because we have developed our proprietary algorithms, and they were developed even before we entered into this market. These proprietary algorithms can read, visualize and analyze big and complex data in a very efficient way.
We can read data from any machine. We can actually handle any data format independent of the type or the dimension, and when acquiring the data during the build process, there are several different data formats, point clouds, volumes, 2D measurements. We can gather all this different data into one single digital twin. That makes it easy for the end user to have a holistic view of all the data that has been gathered during the build process, from the design file to the data that is gathered during the printing process and also, during the post-inspection, for example, with CT scans and so on.
TCT: We hear a lot about ‘AI-driven’ technologies. How is AI being leveraged here?
IH: The proprietary technology of Interspectral from the beginning was very much about handling large data, visualizing large data in an efficient way. Now we are complementing that technology with AI-driven technologies. The AI component that we are developing is actually for optimizing the error detection in the build job, for example, so that we can automate, and make it easier for the end user to use the additive manufacturing in production. We are optimizing the way to get the right answers in the right time.
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TCT: What unique challenges does AM present for manufacturers in terms of quality assurance?
IH: Specifically for the parts that are produced with AM, they are [often]times more complex and they have more complex interiors. Then it's also more challenging for traditional QA methods. So then there needs to be development of more sophisticated QA processes, required for this type of quality assurance. At the same time, additive manufacturing, and specifically LPBF and EBF, are fully digital processes and the process parameters are monitored during the full production process. A lot of data is created, up to several terabytes per build job, and I think that the challenge in terms of quality assurance in this industry is very much about how to, in an efficient way, handle this large amount of data that is being measured during the process, and then also to understand and be able to draw the conclusions from that data.
TCT: Are users doing enough with their data?
IH: As we say and experience every day at Interspectral, there is no lack of data. There is a lack of good tools to visualize, understand, and gain insights from the data that has been generated during the full process - pre-process, during the process and after the process. We believe that there is still a lot to discover within the data that has been measured, and we are providing the tools for this discovery and the big data handling, the standardization, and producing efficient workflows.
Editor’s note: This interview has been edited for brevity and clarity.
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