Physics-AI · Metal Additive Manufacturing

Print it right the first time.

The cloud Physics-AI platform for metal AM. Predict melt-pool, microstructure and residual stress before you print — then tune the process to build it faster.

StressForge · σ_vM + distortion · SS316L · layer-by-layer
6
Connected solvers
5M
Element ceiling
4
CAD formats in
0
Local installs
§ 01

Learn once. Predict at part scale.

High-fidelity physics is too slow to run over a whole part — so we learn it once, then predict in minutes.

01 · Simulate

High-fidelity physics

Melt-pool thermal history and solidification microstructure — the ground truth.

FusionCoreGrainPath
02 · Learn

Train the surrogate

Thousands of runs swept across the process window, fit to a Physics-AI model.

FusionMapGrainMap
03 · Predict

Part scale, in minutes

Residual stress and distortion across the whole part — no meso-scale solve in the loop.

StressForge
04 · Optimize

Close the loop

Search the process window to cut stress and distortion while holding build rate.

ProcessPilot
Zone segmentation · +Z build
S1 · PathWeaver

Scan path, resolved to every voxel

An impeller voxelised and segmented into manufacturing zones — bulk, contour, up- and down-skin — the per-region plan the scan generator hands downstream.

Voxels266,499
Voxel size0.5mm
Contour zone25%
Up / down-skin17%
Build layers84
Temperature · layer by layer
S2 · FusionCore

Melt-pool thermal history

A three-layer LPBF build solved on a graded mesh — the laser melt pool sweeping each track while fresh powder layers deposit above it.

Melt-pool width208µm
Melt-pool depth173µm
Laser power200W
Scan speed1000mm/s
MaterialSS316L
RVE grains · 360°
S3 · GrainPath

Solidification microstructure

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.

RVEs sampled5
Mean grain size53.5µm
Grains / RVE~830
CA cells375,000
MaterialSS316L
Progressive layer activation
Stage 03 · StressForge prediction

Residual stress & distortion, whole part

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.

Max distortion171µm
Peak von Mises611MPa
Yielded volume23%
Build layers62
MaterialSS316L
§ 02

One connected pipeline

Geometry → verdict

Each stage consumes the contract the stage before it emits. No manual handoff, no re-exporting between tools.

CLOSED-LOOP PROCESS OPTIMIZATION trajectory G · R fields stress trains trains surrogates PathWeaver S1 · Scan path FusionCore S2 · Melt pool GrainPath S3 · Microstructure StressForge S4 · Residual stress CertifyAM S5 · Qualification FusionMap Thermal surrogate GrainMap Grain surrogate ProcessPilot S6 · Optimize
Solvers S1–S6 · Internal surrogate engines · Select a stage to open it
§ 03

Minutes, not days

What changes when the whole chain lives in one cloud platform and the expensive physics is learned up front.

Residual stress
Minutes
Part-scale via surrogates — vs. days of chained solves.
Deployment
Zero installs
Runs in a browser tab — vs. a workstation and a licence server.
Qualification
Before you print
Defect maps and pass/fail gates — vs. trial builds.
§ 04

Where it has to be right first time

Aerospace

Flight-critical parts, qualified before the build.

Medical

One-off titanium implants, right without trial prints.

Tooling

Pre-compensated distortion in conformal-cooling tools.

Energy

Integrity confirmed under extreme thermal load.

§ 05

CAD to qualified build

In the browser
Step 01
Upload & set the process

Import CAD, define the LPBF strategy with surface-based selection.

Step 02
Generate on the cloud

Scan path and simulation domain built on elastic infrastructure.

Step 03
Run & qualify

Solve, then explore defect maps and qualification results online.

Also in SolidNetics

The Core physics module

Beyond additive — general solid mechanics on the same cloud: finite elements, physics-informed neural networks and peridynamics.

Metal AM, simulated end to end

Stop qualifying by trial and error.

Predict melt-pool, microstructure, residual stress and defect risk before you commit powder and machine time.