Nano Dimension Brings Simulation Software into the Fold

Additive Flow acquisition delivers the power of simulation for optimizing AM and advanced manufacturing workflows for electronics applications.

Additive Flow acquisition delivers the power of simulation for optimizing AM and advanced manufacturing workflows for electronics applications.

In a nod to the crucial role of 3D design and process simulation software for advancing production-grade additive manufacturing (AM) for electronics applications, Nano Dimension acquired the technology and intellectual property of Additive Flow, an upstart provider of high-performance simulation software.

Additive Flow, established in 2017 to develop advanced digital solutions aimed at optimizing AM workflows, sought to fill a gap in the market for simulation capabilities that could help unlock the multimaterial, multi-parameter process capabilities of AM. The company’s AI and GPU-accelerated software simulates mechanical, thermal, thermo-mechanical, frequency, and fatigue properties across a range of materials and 3D processes, helping engineers simultaneously optimize for cost, weight, manufacturing productivity, and yield. Additive Flow has targeted its software suite to a variety of industry segments, including electronics, aerospace & defense, semiconductors, advanced energy, and advance optics & optoelectronics.

Through use of AI and GPU acceleration, the Additive Flow platform enables optimization that addresses typical AM bottlenecks and hurdles, including the ability to increase production speeds without compromising materials requirements and generating insights used to drive build parameters that result in high-quality outcomes. The software leverages existing CAD data and engineering requirements to seamlessly generate tool paths that aren’t subject to the risks typically associated with poor conversions or data loss, according to Additive Flow officials.

Nano Dimension called its latest acquisition a “successful milestone” in its product development roadmap and acknowledgement that CAE and design components are a crucial part of its strategy to fully deliver on the promise of AM, particularly in the electronics space.  More specifically, Nano Dimension officials said the Additive Flow software stood out for its holistic approach to enhancing the ability of customers to replicate, design, and augment additively manufactured electronics (AME) prototypical layouts in a rapid and cost-effective manner. Additive Flow’s core technology will be integrated into FLIGHT, Nano Dimension’s proprietary design package suite.

“Our solution was born out of real engineering challenges we faced to harness the capability of advanced manufacturing,” said Alexander Pluke, Additive Flow’s CEO and CTO. “Our platform is built from the ground up to handle all the complex data and decisions of digital engineering, so teams can focus on their key goals.”

Nano Dimension also called out Additive Flow’s AI-enabled capabilities as a fit with its strategy to apply deep learning-based AI to drive self-learning and improvements to AME processes. The Additive Flow technology will complement and enhance the AI learning capabilities Nano Dimension acquired through its DeepCube acquisition in 2021. DeepCube, a division of Nano Dimension, is a training and real-time inference engine that will be integrated into Nano Dimension 3D printers as a foundation for an AI-powered AME platform and set of services. The DeepCube algorithms accelerate multi-domain neural models and are a fit for complex and real-time edge applications, including smart 3D manufacturing nodes.

The Additive Flow acquisition is Nano Dimension’s first since backing away from its hostile takeover attempt of 3D printing giant Stratasys.


Watch this webinar video to learn more about how Additive Flow’s AI-enabled technology is changing AME workflows to deliver high-quality production results.

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Beth Stackpole's avatar
Beth Stackpole

Beth Stackpole is a contributing editor to Digital Engineering. Send e-mail about this article to [email protected].

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