The cloud-native Physics-AI platform for metal additive manufacturing. Surrogate models — trained on thousands of high-fidelity simulations — carry a part from scan path through melt-pool, microstructure and residual stress to a certification-ready report, then optimize the process to print it faster. No installs, no meshing bottlenecks, no trial builds.
Full-physics AM simulation is accurate but far too slow to run over a whole part. SolidNetics trains physics-informed surrogate models on thousands of high-fidelity simulations, so part-scale residual stress, distortion and microstructure that once took days run in minutes — fast enough to optimize the process in a closed loop.
FusionCore resolves the melt-pool thermal history and GrainPath resolves solidification microstructure — the ground truth the AI learns from.
FusionMap and GrainMap run thousands of those thermal and microstructure simulations and train physics-informed surrogate models from the results.
StressForge applies the trained surrogates to predict residual stress and distortion across the entire part — at full scale, with no meso-scale solve.
Because prediction is now fast, ProcessPilot searches the process window to cut residual stress and distortion while keeping the build fast.
SolidNetics replaces a desktop of disconnected tools with a single browser tab built for additive manufacturing. Import a CAD part, define the process strategy visually, and run the whole LPBF chain on scalable cloud infrastructure — from a first feasibility check to a full build qualification.
No installation, no licence servers, no hardware. CPUs and GPUs scale on demand, and every update ships automatically.
Import STL, STEP, IGES or OBJ. Define the LPBF process strategy with surface-based selection and automatic normal detection.
One connected pipeline carries a build from laser trajectory through to a qualification verdict — no manual handoff between stages.
Six solvers and two internal Physics-AI engines carry a part from geometry to a certification verdict — then ProcessPilot closes the loop by tuning the process to reach it faster. Each stage consumes the previous stage's contract — no manual handoff, no re-exporting between tools.
// geometry · process strategy → trajectory → melt-pool → microstructure → residual stress → qualification → process optimization
Turns a part and a process strategy into a time-stamped laser trajectory — the single contract every downstream AM solver reads.
High-fidelity meso-scale thermal simulation of LPBF — resolving melt-pool geometry, solidification gradients and phase evolution.
High-throughput simulation engine that trains the surrogate models powering StressForge and GrainPath. Not exposed as a standalone tool.
Predicts solidification microstructure, grain morphology and columnar-to-equiaxed transitions from thermal-gradient and solidification-velocity fields.
Runs thousands of GrainPath microstructure simulations to train the surrogate models that bring grain prediction to part scale — the microstructure counterpart of FusionMap. Not exposed as a standalone tool.
Part-scale residual stress, warping and distortion in LPBF builds, using physics-informed surrogates trained on high-fidelity thermal data.
Consolidates thermal, microstructure and mechanical predictions into defect-probability maps, property estimates and certification-ready quality reports.
Closes the loop: drives the trained Physics-AI surrogates across the LPBF process window to minimize residual stress and distortion while maximizing build-rate — returning optimized, region-aware process parameters.
Three steps, entirely in the browser.
Import a CAD model and define the LPBF process strategy — orientation, laser parameters and scan strategy — through surface-based selection.
Scan path and simulation domain are built on scalable cloud infrastructure — no local hardware or meshing bottlenecks.
Solve melt-pool, microstructure and residual stress across CPUs and GPUs, then explore defect maps and qualification results online.
Built to remove the trial-and-error between a CAD part and a validated, qualified build.
The only path from LPBF scan path through melt-pool, microstructure and residual stress to certification — fully connected, one workflow.
See melt-pool behaviour, distortion and defect risk before committing powder and machine time — instead of qualifying by repeated builds.
Surrogates trained by FusionMap and GrainMap on thousands of high-fidelity runs return part-scale microstructure and residual-stress predictions in minutes, not days — and ProcessPilot optimizes the process on top of them.
STL, STEP, IGES and OBJ in; visual process setup; everything runs in the cloud with no installation and no workstation.
Defect maps, property estimates and pass/fail gates are consolidated into quality reports built for part qualification.
New solvers, machine profiles and validation checks roll out automatically — you always run the latest version.
Where metal additive manufacturing has to be right the first time — the problem each sector faces, and how SolidNetics solves it.
Flight-critical parts — brackets, fuel nozzles, turbine components — must be fully traceable. Residual stress and hidden defects cause distortion and rejected builds, and qualification by repeated trial prints is slow and costly.
Predicts melt-pool, microstructure, residual stress and defect probability before printing, and produces certification-ready reports that de-risk and shorten qualification.
Every patient-specific titanium implant is a unique geometry that can't be trial-printed, yet it must meet exact porosity, fatigue and dimensional targets.
Simulates microstructure and residual stress for each patient geometry, guaranteeing mechanical performance and dimensional accuracy without iterative builds.
Conformal-cooling tools warp and crack under residual stress. A failed insert means lost powder, machine hours and lead time on an expensive build.
Predicts distortion and residual stress up front so geometry can be pre-compensated — delivering right-first-time tools and fewer rebuilds.
Lightweight performance and EV parts need fast iteration, but porosity and build failures stall the move from prototype to series production.
Surrogate-accelerated simulation screens process parameters in minutes, cutting scrap and speeding qualification for production-rate AM.
Heat exchangers, turbine and nuclear components run in extreme thermal and pressure conditions where defects or residual stress threaten integrity.
Generates defect-probability maps and residual-stress fields so engineers can confirm structural integrity and long-term reliability before the build.
Process–structure–property studies need high-fidelity thermal and microstructure data that is slow and expensive to produce experimentally.
Cloud, physics-based and surrogate solvers generate G/R, microstructure and residual-stress datasets at scale — no lab time or workstation required.
Stop qualifying parts by trial and error. Predict melt-pool, microstructure, residual stress and defects before the build — and ship certification-ready reports from a single cloud platform.