The cloud Physics-AI platform for metal AM. SolidNetics predicts distortion, residual stress and microstructure for laser powder-bed fusion — melt-pool physics and grain structure feeding the part-scale answer directly, in a browser, in minutes.
One physics chain spanning five orders of magnitude — from the laser spot to the part on the plate.
Transient thermal solve of the laser–powder interaction, per parameter set — not a fitted heat input.
Grain growth through solidification — size, texture, and the anisotropic stiffness and yield that follow.
Layer-by-layer solve over the full geometry, driven by the two scales before it.
Everywhere else, these are separate products. Parameter development sits in one tool, part distortion in another, and the microstructure never reaches the mechanical answer. We build them as a single chain — each stage consumes what the last one emits, with no re-export and no manual handoff.
High-fidelity physics is too slow to run over a whole part — so we learn it once, then predict in minutes.
Melt-pool thermal history and solidification microstructure — the ground truth.
Thousands of runs swept across the process window, fit to a Physics-AI model.
Residual stress and distortion across the whole part — no meso-scale solve in the loop.
Search the process window to cut stress and distortion while holding build rate.
An impeller voxelised and segmented into manufacturing zones — bulk, contour, up- and down-skin — the per-region plan the scan generator hands downstream.
A three-layer LPBF build solved on a graded mesh — the laser melt pool sweeping each track while fresh powder layers deposit above it.
Five representative volumes sampled across the part's thermal clusters, each solved cell by cell into a full grain structure — coloured by thermal cluster, grain size and columnar fraction.
One SS316L bracket, solved layer by layer on the cloud — the part-scale field you'd otherwise wait on a meso-scale simulation to see.
Each stage consumes the contract the stage before it emits. No manual handoff, no re-exporting between tools.
What changes when the whole chain lives in one cloud platform and the expensive physics is learned up front.
Why the chain is built this way — and what it means for the shop floor.
We run the melt-pool and microstructure physics once across the process window and train surrogate models on it. Part-scale predictions then evaluate the surrogate instead of re-solving — so a whole part takes minutes, and changing parameters doesn't mean starting over.
Predicted grain size and texture set the anisotropic stiffness and yield used in the part-scale solve. Not a separate report — an input. The same chain carries process conditions through to properties.
Nothing to install, no workstation, no CAE specialist to keep it running. Elastic cloud compute, pay for what you run, and a price a production shop can approve without a procurement cycle.
Runs execute in a single named region, encrypted and isolated per account, and are never used to train models shared with anyone else. Deleted on request, NDA before first upload. Private-VPC deployment on the roadmap.
ProcessPilot searches the process window for the parameter set that minimises distortion at a held build rate. Thousands of trial evaluations are only affordable because the surrogates answer instantly — the same architecture that makes one part fast makes optimisation possible.
Flight-critical parts, qualified before the build.
One-off titanium implants, right without trial prints.
Pre-compensated distortion in conformal-cooling tools.
Integrity confirmed under extreme thermal load.
Import CAD, define the LPBF strategy with surface-based selection.
Scan path and simulation domain built on elastic infrastructure.
Solve, then explore defect maps and qualification results online.
Beyond additive — general solid mechanics, heat transfer and design optimization on the same cloud: finite elements, physics-informed neural networks and peridynamics.
Predict melt-pool, microstructure, residual stress and defect risk before you commit powder and machine time.