About SolidNetics

Our MissionPrint it right the first time

SolidNetics is a cloud Physics-AI platform for metal additive manufacturing. Predict melt-pool, microstructure and residual stress before you print — then tune the process to build it faster, with no meshing bottlenecks and no local hardware.

Founded 2025 · cloud-native · Physics-AI · LPBF · melt pool · microstructure · residual stress · qualification

01

Our story

Founded in 2025, SolidNetics is a cloud-native Physics-AI platform for metal additive manufacturing — predicting how a laser powder-bed fusion (LPBF) build will behave before any powder is melted: scan path, melt-pool thermal history, solidification microstructure, and part-scale residual stress and distortion.

Metal AM promises complex, high-value parts — but qualifying a build is slow and costly, dominated by trial-and-error prints and after-the-fact inspection. The high-fidelity physics that could predict the outcome is far too slow to run over a whole part, so it rarely makes it into the design loop.

SolidNetics closes that gap with a connected pipeline and a Physics-AI approach: run high-fidelity meso-scale physics once, learn it into fast surrogate models, then predict at part scale in minutes. Upload geometry, choose a stage, and run on the cloud — no meshing bottlenecks, no local hardware.

We are a multidisciplinary team of mechanical engineers, computational scientists, and ML & software developers with one mission: make metal AM predictable, so the first build is the right build.

Founded
2025
cloud-native from day one
AM pipeline
5
scan path → qualification
Element ceiling
5M
part-scale on the cloud
Installation
0
runs in any browser
02

How SolidNetics predicts your build

High-fidelity physics is too slow to run over a whole part — so we learn it once, then predict in minutes. Three ideas make that work, unified in one automated cloud workflow.

High-fidelity meso-scale physics

The ground truth starts with resolving the process itself — the moving laser melt pool and the microstructure that solidifies behind it — at the scale where the physics actually happens.

  • FusionCore — transient melt-pool thermal history
  • GrainPath — solidification grain structure & texture
  • Multi-layer builds on graded meshes, resolved layer by layer
  • Finite-element solvers scaled on AWS Batch

This is the expensive, accurate physics that every downstream prediction is anchored to.

Melt pool Microstructure Multi-layer Meso-scale

Physics-AI at part scale

Running meso-scale physics over a whole part is intractable. So we sweep thousands of high-fidelity runs across the process window and learn them into fast surrogate models — Physics-AI that predicts in minutes what a full solve would take days to produce.

  • FusionMap & GrainMap — train the surrogates
  • StressForge — part-scale residual stress & distortion
  • No meso-scale solve left in the design loop
  • Up to 5M elements, solved on the cloud

The accuracy of high-fidelity physics, at the speed a design iteration needs.

Learn
surrogates
swept process window
Predict
part scale
in minutes
Output
stress
& distortion
Scale
5M elem
cloud-scaled

One connected pipeline

Every stage consumes the contract the stage before it emits — geometry to verdict, with no manual handoff or re-exporting between tools. The whole chain lives in one cloud platform.

  • PathWeaver → FusionCore → GrainPath → StressForge → CertifyAM
  • ProcessPilot closes an optimization loop back to the start
  • Qualification-ready reports, not just fields
  • Core Physics add-on: FEA, PINN & peridynamics for general solid mechanics

From a CAD upload to a print-or-not verdict — one platform, end to end.

Scan path Connected Closed-loop Qualification
03

How SolidNetics works

Upload a part, and the pipeline predicts the build — scan path, melt-pool thermal, microstructure, and part-scale residual stress — every stage on automated cloud infrastructure.

FIG.01 · melt-pool thermal · FusionCore geometry in · cloud predict · verdict out
04

Technology & infrastructure

A single cloud-native framework built end-to-end on AWS for scalability, reliability, and speed.

Powered by AWS provisioned per-solver · scales with demand
Simulation engine

Meso-scale melt-pool and microstructure solvers, part-scale residual-stress prediction via Physics-AI surrogates, and — for general solid mechanics — FEA, PINN and a C++ peridynamic engine, all in one cloud framework.

Cloud infrastructure

Runs entirely on AWS for scalability, reliability and speed. Launch simulations from any device with no local installation.

Data storage

AWS DynamoDB manages user and project metadata; inputs and results are stored securely in S3 in structured formats including JSON and HDF5.

Preprocessing

AWS Lambda automates point-cloud generation from CAD geometry (STL, STEP, IGES, OBJ) and normalizes geometry and boundary conditions.

Job queue

AWS SQS handles asynchronous job dispatch, ensuring smooth communication between the web application and compute backends.

Compute instances

AWS Batch provisions the right instance per solver — CPU and high-memory for meso-scale and part-scale AM jobs, GPU for surrogate and PINN workloads — pre-configured with the SolidNetics runtime and scaled dynamically.

05

Mission & vision

Our Mission
Make metal AM predictable

To replace trial-and-error prints with prediction — using high-fidelity physics and Physics-AI to forecast melt-pool, microstructure, residual stress and distortion before a build starts, so engineers qualify metal AM parts faster, cheaper, and with confidence.

predict before you print
Our Vision
Every build simulated before it prints

A future where no metal AM part is committed to powder and machine time without first being simulated end to end — where process design is a closed optimization loop, not a sequence of failed coupons, and advanced AM simulation is accessible to every engineering team.

more speed · more accuracy · more design freedom
06

Our core values

The principles behind a cloud platform built for modern engineering teams.

Accessibility

Access your data and tools from anywhere with an internet connection.

Collaboration

Multiple users can work on the same project simultaneously, increasing efficiency and reducing errors.

Scalability

The platform accommodates increased usage and data storage as your work grows.

Cost savings

No expensive hardware or IT infrastructure required — advanced analysis without the overhead.

Data security

Secure storage and backup of important data, reducing the risk of loss or theft.

Improved performance

Powerful servers and infrastructure ensure fast, reliable performance.

Software updates

Regular updates roll out without disruption, keeping the platform current and secure.

07

Our team

SolidNetics was founded by a diverse team spanning additive-manufacturing and metallurgy, computational solid mechanics, machine-learning and AI engineers specializing in physics-informed and surrogate models, and computer scientists — merging process physics with modern AI, cloud computing, and product design.

We are actively growing. If you work in additive manufacturing, materials, solid mechanics, machine learning, AI, software engineering, or cloud infrastructure, we'd love to hear from you.

Build the future of additive manufacturing with us

From research labs to production floors, SolidNetics makes metal AM predictable — melt-pool, microstructure, residual stress and qualification, before you commit powder and machine time.