PanX - A Novel and Unique FEA Software Architecture for AM
PanX is Accurate.
- Resolve fine geometric details
- Model full build plates (location and interaction of parts)
- Achieve high accuracy thermal predictions (loose powder& weighted average interlayer temperature approach)
- Include actual machine process parameters and timing
- Perform high accuracy calibration
PanX is Scalable.
- Model any geometry regardless of size and complexity without having to defeature the part
- Scale computational performance and runtime linearly with model size, as opposed to cubically in traditional FEA tools
PanX is Fast.
- Simulate even the largest and most complex geometries in a small fraction of the actual build time
- Simulate 100x - infinitely faster than legacy FEA tools
PanX is Efficient.
- Most simulations can be run on an engineering laptop (8 cores, 64Gb RAM)
- The most complex geometries (e.g. heat exchangers) can be run on engineering desktops (48 cores, 500Gb RAM)
25,874,981 Elements
54,806,663 Nodes
206 GB of RAM
48 Core Desktop
160 Hours to Print
14 Hour PanX Solve
How can PanX achieve higher accuracy and superior performance than all other available software solutions?
Efficient and Scalable Architecture
New Approach to Solving Additive Problems
PanX was designed from the ground up to maximize accuracy and scalability.
PanX re-thinks how AM problems are modeled and utilizes a new set of core technologies to achieve high accuracy at any problem size
Multi-Grid Modeling
Traditional Adaptive Meshing
Elements are activated at the fine resolution, but coarsened throughout the analysis. This erases detail with each coarsening generation
PanX Multi-Grid Modeling
Each new layer brings new refined elements and coarsens previous layers.
The fine grid result is a combination of all of the intermediate grids
Accuracy Improvement​
Since the transient detail is not lost, MGM can resolve fine detail (build lines and stress concentrations) with the same fidelity as a fixed fine grid!
Feature Aware Meshing
Traditional Voxel Mesh
Traditional meshing can capture fine features; however, tends to dramatically over-refine where fine detail is not needed
Feature Aware Mesh
Feature aware meshing maintains fine details where needed, but allows for coarsening in less interesting (bulkier) areas of the model
Large Part Meshing
Efficient Meshing
PanX leverages periodic adaptivity and can easily handle meshes in the 100s of millions of elements.
This "relatively small" 70,934,134 element heat exchanger meshes and solves in 9.2 hours on a 48 core pc
Fast and smart
Feature aware geometry processing lets PanX resolve ultra small features while coarsening elements in bulkier areas where the detail is less critical
Large Part Scaling
Start fast...End Fast
Legacy additive simulation tools solve the first few layers very quickly, and then slow to a crawl as elements are added
PanX Multi-Grid Modeling with advanced meshing eliminates this exponential slowdown and enables models to run faster over the entire solve.
Dramatically reduced RAM
Besides solve speed, PanX Multi-Grid Modeling with advanced meshing keeps compute resources to a minimum, even for our extra large heat exchanger, which was has almost 283 million elements (and 570 million nodes)
High Definition Surface Results
Surface vs Voxel Results
PanX leverages the Multi-Grid approach to map surface results from extremely fine voxel data. ensuring the best possible data resolution.
Warped Results Tell the story
Coarse elements and large macro layers inevitably smooth your results and hide the true complexity of the part behavior.
Here the fine multi-grid mesh shows multiple build lines, warps, and kinks, while the coarse result shows very little useful information.
Smooth Surface Stresses
Mapping data from a coarse mesh can result in very little stress concentration in the final result. Because everything in PanX operates on the multi-grid concept, the surface results have excellent accuracy.
Adjoint Sensitivity Driven Optimization
Optimize Any Model Input
PanX computes the adjoint sensitivities (derivatives of each model output with respect to each input), which enables fast convergence of even large optimization problems
Example analysis showing PanX optimization used to minimize support structure volume while not exceeding the maximum temperature constraint