3D printing software developer Dyndrite has partnered with Ansys, now part of Synopsys, to integrate advanced thermal simulation capabilities into metal AM workflows.
The collaboration combines Ansys’ thermal processing simulation tools with Dyndrite’s Laser Powder Bed Fusion (LPBF) Pro software. Its purpose is to help engineers predict and manage thermal effects that influence the quality and reliability of metal parts produced using additive manufacturing.
Thermal behavior has long been one of the tougher challenges in metal 3D printing. When heat builds up unevenly during a print, it can cause parts to warp, develop internal stresses, or form inconsistent microstructures, issues which can ultimately affect performance.
By embedding Ansys’ simulation technology into Dyndrite’s print preparation tools, engineers can identify potential issues before printing begins. These predictive insights help reduce failed builds, save material and production costs, and minimize downtime, particularly for large or complex components.
The integration also aims to improve consistency across machines and materials, with Dyndrite’s Python APIs enabling engineers to codify and replicate proven print strategies for reliable, repeatable results.
“We’re excited to collaborate with Ansys to unlock new capabilities for our customers and theirs,” said Harshil Goel, Founder and CEO of Dyndrite. “By combining Dyndrite’s powerful toolpathing control with Ansys predictive thermal simulations, we can help manufacturers increase confidence and accelerate the adoption of metal AM for mission-critical applications.”
Advancing thermal simulation for metal AM
Ansys’ thermal simulation technology is part of a broader multiphysics suite that integrates thermal, structural, fluid, and electromagnetic analyses to model complex interactions in real-world manufacturing environments.
In additive manufacturing, the tools simulate melt pool behavior, cooling rates, and thermal distortion in processes like LPBF, Electron Beam Melting, and binder jetting. By predicting heat distribution and stress formation across layers, engineers can detect potential warping or uneven cooling in advance, using a materials database of over 20,000 entries to ensure accurate modeling across alloys and conditions.
In an interview, Scott Parent, VP and Field CTO of Aerospace, Energy and Industrial at Ansys, said the company is also combining thermal simulation with in-situ measurement and machine learning to improve accuracy and efficiency. This integration enables real-time anomaly detection and ongoing optimization of print parameters, reducing build failures and development time.
Together, these capabilities help manufacturers improve process control, achieve consistent part quality, and accelerate the industrialization of metal AM across sectors such as aerospace, energy, and automotive.
Within this context, Dyndrite’s collaboration with Ansys could also streamline part qualification. Simulating how process parameters influence material properties allows teams to validate designs digitally, reducing the need for repeated physical test prints. This approach can shorten development timelines and support certification of high-performance components used in aerospace, defense, and energy applications.
Looking ahead, the companies plan to advance the integration so engineers can apply simulation results directly within Dyndrite LPBF Pro. This would allow users to fine-tune toolpaths and process parameters based on predicted thermal behavior, improving build consistency across machines and production sites.
Both companies are also engaging early LPBF users for feedback to guide the integration and demonstrate how simulation-driven build preparation can reduce risk and accelerate part development.
New approaches refine simulation in metal AM
Across the AM landscape, engineers are combining physics-based modeling with artificial intelligence to better understand and control how metals behave during printing.
Researchers from Arizona State University’s (ASU) School of Computing and Augmented Intelligence developed an AI-driven system to predict how a metal’s microstructure and strength evolve during 3D printing.
Funded by the National Science Foundation’s (NSF) CompAM project, the work combined physics-based models with machine learning to create faster, more targeted simulations of thermal and structural behavior in stainless steel parts. Tested on a 316L stainless steel propeller produced with a six-axis robotic 3D printer, the method aimed to cut simulation time, reduce trial-and-error, and improve accuracy in metal additive manufacturing.
In 2019, California-based Velo3D enhanced its Flow print preparation software for its Sapphire System LPBF metal AM platform. The updated Flow featured a physics-driven simulation engine optimized for Sapphire, enabling predictive modeling of print outcomes and achieving first-print success rates of up to 90%.
It incorporated tools for part orientation, support generation, and deformation correction to ensure dimensional accuracy and reduce preparation time. The software also applies optimized print processes to specific geometric features and allows support-free printing of complex geometries, such as 5° overhangs and 40 mm tubes, streamlining production and minimizing post-processing.
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Featured image shows Dyndrite LPBF Pro streamlines materials and process development. Photo via Dyndrite.
