nebumind is in the news – digital thread vs. digital twin


Digital thread vs. digital twin – Ginger Gardiner explains in her article how nebumind alongside Deloitte, Profactor, Kanfit, Dynexa, Plataine and DLR supports the digital transformation and adaptive production in the composite manufacturing world.

The article demonstrates the diversity of digital applications and the different aspects addressed by the applications.

The following is an extract, you can read the full article here.


Digital twin for product vs. production

And this brings us to one final point: a digital twin can be developed not just for a product but also for its production — in other words, for each production line or factory. To differentiate between product and production digital twins, I will explore new software by nebumind (Munich, Germany).

digital twin of product and production and performance
Source | Siemens PLM

Like Plataine, nebumind supplies IoT software for manufacturing that enables a digital twin of the part produced by machines. “Our specialty, however, is to map production data onto every location of the part,” explains Franz Engel, nebumind co-CEO and co-founder with Caroline Legler. Both CEOs previously managed the Airbus subsidiary InFactory Solutions, developing sensors for AFP inline inspection and resin infusion. “Many digital twins are of the production,” Engel continues, “analyzing manufacturing flow and efficiency. Ours is a more part-centric view.”

nebumind data temperature speed rotation at defect position
Source | nebumind

nebumind’s software builds a 3D model of each part produced and adds all machine and sensor data generated during its manufacture. For now, the software works with CNC and robot-based processes such as AFP, 3D printing and milling/drilling. “For every machine position on the part, we collect the process data such as temperature, speed and pressure,” explains Engel. “For all data that is recorded, we collect and store also the time when it was recorded and, more importantly, the location or position on the part where it was recorded. Now, when we analyze part quality and see a defect at point x, y, z, but only on every fifth part, we can look in our software at all of the parts produced and see all of the parameters for just that location. We can see how the parameters compare between parts with and without defects, and also how that location compares to the rest of the part. In this way, we can trace back quality defects to their origin and control part quality across manufacturing process steps.”

“nebumind is geared toward providing a high level of granularity for a single process,” says Florian Krebs, team leader for flexible automation at the Center for Lightweight Production Technology (ZLP, Augsburg, Germany). Krebs is part of the team that has developed an AI-equipped work cell where collaborative robots can switch from producing composite rear pressure bulkheads to fuselage panels without requiring reprogramming or retraining (see main article and “No business case for reteaching robots”). “nebumind provides location-based data collection and storage, as well as automated analytics to predict part quality and gain confidence in causality of that quality. It also enables a visual representation of the data. We are looking at integrating nebumind into our central storage to help with our reports.

nebumind process data per position with mean std. deviation and time stamp
Source | nebumind

That central storage is the digital twin.“The most important point of digital twin is to have one central repository, one source of truth,” says Dr. Michael Kupke, head of the ZLP Augsburg. The diagram below shows the data architecture for the ZLP’s flexible automation platform that allows the AI-equipped work cell to build a CFRP rear pressure bulkhead (upper right corner) or fuselage panel (lower right) and switch between quickly. Note the central storage/repository is the gray disk at center marked Data Base. “We collect all of the data into this central repository to improve process quality but also for visualization,” says Krebs.

Source | DLR Institute for Structures and Design

For the PROTEC NSR and Factory of the Future project, ZLP developed a flexible automation platform that can produce a CFRP rear pressure bulkhead (top right) or fuselage panel (bottom right), and switch between these quickly by simply changing the CAD file. For enlarged image, see main article.

“There are many software solutions to collect data and put it in a digital twin,” says Krebs, “for example from Google, Amazon and Microsoft. The key is to tailor these to your requirements.” Engel contends these solutions don’t go as deep into the process of making the composite part as nebumind does. “We help to understand defects and find the root cause. We make it easy and fast to identify the source of the defect in the manufacturing data and also to monitor this online. For example, you can set an alarm if temperature in the upper right corner exceeds 100°C.”

“Plataine is trying to improve the entire part-making process,” says Ben-Bassat. “nebumind could provide a very useful data source that we could roll in to make the digital thread richer and more informed.

Stay tuned for my full blog on nebumind, and, I’m sure, future updates on Plataine, ZLP, AZL and other providers of Composites 4.0 tools and solutions.