USA & Canada
USA & Canada
Upcoming Webinar

Your FDM Parts Are Stronger Than Your Simulation Says They Are.

Join Novineer and Stratasys engineers for a live technical webinar on toolpath-aware FEA simulation for FDM/FFF -- and learn how to get predictions within 5 to 18% accuracy instead of 120% off.

Calendar 6/17/2026 Time 11:00 AM CDT Duration 1 hour

Reserve My Spot

Presented by engineering teams from Novineer and Stratasys. No sales pitch. Technical content built for engineers who need accurate simulation, not approximations.

If you design and validate FDM/FFF parts, you already know the simulation problem.

General-purpose FEA treats your printed part like a uniform block of plastic. It ignores bead orientation, layer bonding, and toolpath geometry -- the very things that determine how your part actually performs. The result: prediction errors that routinely exceed 120%, forcing engineers into overbuilt designs or endless print-and-break cycles.

NoviPath takes a fundamentally different approach. It imports your actual toolpaths directly from GrabCAD Print Pro and pairs them with experimentally validated material cards -- so the simulation reflects how your part is really printed, not a simplified approximation.

IN THIS SESSION, YOU WILL LEARN:

  • Why traditional FEA fails for FDM and how much accuracy you are actually leaving on the table
  • How toolpath-aware simulation works and what makes it meaningfully more accurate
  • A live walkthrough of the GrabCAD Print Pro + NoviPath integration (slice, import, simulate, optimize, print)
  • A real case study showing 120% FEA error corrected to under 18% -- with a 40% lighter final part
  • Supported materials: Nylon 12CF, Antero 800NA, Ultem 9085

RESULTS ENGINEERS ARE SEEING WITH NOVIPATH:

  • 85% reduction in lead time
  • 40% lower production costs
  • 40% weight reduction on optimized parts

35% more FDM applications unlocked  

Your Speakers
Frank Lindeman
Frank Lindeman
Business Development & Customer Success| EMEA Stratasys Software

Frank Lindeman joined Stratasys in 2014, when the cloud PDM and file sharing company GrabCAD was acquired. Before GrabCAD Frank was with 3D CAD company SolidWorks helping software partners develop a wide range of applications using the SolidWorks API platform. Frank is a member of the Stratasys Software team, a group of software engineers developing next generation software for 3D Printing and Additive Manufacturing, working out of Cambridge in the UK. Today, Frank focuses on making Stratasys customers and software partners successful with the introduction of Stratasys software products, and their integration with design software and factory control software. He also represents the voice of the customer, identifying opportunities for new software features, functions, or entire products. His experience and approach are grounded in both 25 years of development of technical software as well as a deep understanding of design and manufacturing processes. Frank holds an M.Sc. in Mechanical Engineering.

Ali Tamijani
Dr. Ali Tamijani
Co-founder and CEO of Novineer

Dr. Ali Tamijani is Founder and CEO of Novineer, Inc., and Professor of Aerospace Engineering at Embry-Riddle Aeronautical University. A recipient of the AFOSR Young Investigator Award, NSF CAREER Award, and Embry-Riddle’s Outstanding Researcher of the Year Award, he has led Novineer in launching NoviVision, NoviDesign, and NoviPath, products that help engineers turn existing parts and design requirements into production-ready digital models faster and with less risk. NoviPath is integrated with Stratasys' GrabCAD Print Pro as part of a strategic partnership. He has also driven the company’s early growth by securing federal support, building partnerships with OEMs, and establishing corporate agreements with defense organizations including the U.S. Army. 

Jayjayanti
Subramaniam (Jay) Jayanti
Principal Engineer | Novineer

Jay specializes in physics-based modeling, additive manufacturing simulation, and software development. A former Stratasys engineer, he brings hands-on experience across commercial R&D, geometry processing, and machine learning.