Simulation and Optimization for FFF/FDM Printed Parts
Interview with Dr. Ali Tamijani – Novineer
Could you start by introducing Novineer’s work and outlining what you will be presenting at CDFAM?
At Novineer, we develop software for generative modeling, geometric design, and toolpath-aware optimization.
At CDFAM, we’ll present our toolpath-aware design & simulation workflow—how we align bead orientation with load paths to boost stiffness and strength, tune trade-offs (strength vs. time/material)—alongside experimental validation where measured part performance matches the predicted gains.
Your software optimizes FFF/FDM toolpaths based on load paths to improve stiffness and strength. What types of data are used to drive these optimization algorithms?
The inputs are the digital model (e.g., STEP or STL), the applied loads and boundary conditions, printer specification, and material properties.
Can you walk through the data flow from a CAD model to the final optimized toolpath, and where in that process user input is most critical?
Import CAD or mesh file (STEP/STL); select a material from Novineer’s library or enter custom material properties; define load cases and boundary conditions; run structural analysis to compute load paths; set printer/process constraints (layer height, filament width, minimum turn radius, minimum segment length); generate stress-aligned toolpaths and bead packing that satisfy those constraints; optionally analyze an existing job by importing its G-code/CMB.
For a company evaluating Novineer’s software, what characteristics of a part or application should they look for when selecting an initial project?
A strong candidate for evaluation is a tooling or end-use part where stiffness or strength is critical. The traditional alternatives are trial-and-error toolpath adjustments or experimental testing to evaluate performance. Both approaches are costly, time-consuming, and tedious, whereas Novineer streamlines the process by simulating and optimizing toolpaths up front to achieve measurable performance improvements.
What challenges have you encountered in balancing structural optimization with print time, material usage, and manufacturing constraints?
Strategic placement of filament cuts may improve strength but may also increase print time. Likewise, tight turns may clean up the global path yet violate manufacturability constraints (e.g., minimum turn radius, corner quality). Our toolpath optimization balances these trade-offs—strength, time, and process limits—to produce feasible, high-performance paths.
What do you hope to share with and gain from the CDFAM audience regarding the structural optimization of FFF/FDM printed parts?
I’ll share the methodology behind our toolpath-aware optimization, the end-to-end workflow from CAD to 3D printing paths, and experimental validation showing the stiffness/strength gains we’re seeing on parts. I’m looking for feedback, use cases where filament paths are decisive, access to or alignment on benchmark datasets and test cases, and collaborators—OEMs and end-users—interested in pilots and deeper discussions.
