Sector · Aviation and aerospace manufacturing
Physical AI for aviation and aerospace manufacturing
Aircraft parts carry a conformity record for every serialized piece, and that record usually lives apart from the shop floor, the metrology bay and the work order that produced it. These are the questions engineers ask first, answered from 1 published CodeNinja Atoms reference architecture.
- What does a physical AI system for aviation and aerospace manufacturing look like?
- Which AI models can an aviation and aerospace manufacturing operator run on its own hardware?
- How much compute and hardware does AI in aviation and aerospace manufacturing need?
- Is it cheaper to own AI hardware or rent cloud GPUs in aviation and aerospace manufacturing?
- What ontology or object model does an aviation and aerospace manufacturing AI system need?
- Who approves the decisions an AI system makes in aviation and aerospace manufacturing?
What does a physical AI system for aviation and aerospace manufacturing look like?
A complete physical AI design for aviation and aerospace manufacturing names what to sense, which existing systems to join, the object model that joins them, the models and hardware, the three-year cost and the person who approves every action. CodeNinja Atoms has published 1 such reference architecture for aviation and aerospace manufacturing, each free to reuse under CC BY 4.0.
| Design | Country | What it does |
|---|---|---|
| Hangar Form Cell | United States | a vendor-independent dieless robotic sheet metal cell that takes an aircraft structural part from CAD to a certified, serialized part with collision-free springback-compensated toolpaths, in-process metrology and a per-serial traceability record, wholly inside the facility's own boundary with no vendor cloud. |
Which AI models can an aviation and aerospace manufacturing operator run on its own hardware?
Each published aviation and aerospace manufacturing design names its models and why, and every model is open-weight or no model is used at all, so the operator can run it on hardware it owns.
| Design | Models |
|---|---|
| Hangar Form Cell | Anomalib 2.7.0 (Apache-2.0) for surface and geometric anomaly detection on formed panels, and Chronos-Bolt (Apache-2.0) for force-trace baselines, both on the cell edge box; no language model committed |
| Design | Choice | What was picked | Why |
|---|---|---|---|
| Hangar Form Cell | Vision anomaly detection | Anomalib 2.7.0, FP32, exported to ONNX Runtime | Unsupervised fit on the facility's own good panels needs no labeled defect set; Apache-2.0 keeps the trained detector the operator's own |
| Hangar Form Cell | Force baseline model | Chronos-Bolt, Apache-2.0 | Learns per-arm, per-stylus load baselines on the edge box and flags drifting pass signatures |
| Hangar Form Cell | Edge AI compute | Fanless industrial inference computer class | Sized from the camera and sensor stream budget with headroom; bench-measured on the actual streams before order |
| Hangar Form Cell | Facility inference server | Eight-card 48 GB PCIe air-cooled inference server class | Holds the registered models with KV and batch headroom on the facility's own power |
| Hangar Form Cell | Time synchronization | OCP Time Card grandmaster with linuxptp | Sub-millisecond ordering across scan, force and pose streams |
| Hangar Form Cell | One-way transfer | lidi and Eurydice over a hardware data diode | One-way egress from the cell OT zone to the facility tier |
| Hangar Form Cell | Enclosures, mounting and power | Rugged enclosure class with Network UPS Tools | Clean shutdown on power loss; rated for shop-floor temperature and dust |
| Hangar Form Cell | Cameras | Fixed, RTSP-serving class from the facility's approved list | Pixels-on-target density per zone; no export license applies to domestic installation in the United States |
| Hangar Form Cell | Sensing set | Force-feedback load cells at the styli, joint encoders and torque telemetry, a structured-light 3D scanner at the metrology bay, a laser tracker reference channel, frame and fixture temperature sensors, safety PLC and interlock state via MTConnect | Covers force, as-built geometry, thermal drift and interlock state beside every measurement |
| Hangar Form Cell | Platform pattern | On-premises AI platform reference architecture | Two layers with a clean contract, GPUs managed as declarative Kubernetes operators, compute facility-owned on-premise |
| Hangar Form Cell | Robotics pattern | Simulation-first robotics reference architecture | Every asset converted to OpenUSD once; software-in-the-loop path validation before metal is moved |
| Hangar Form Cell | Ground it runs on | K3s at both tiers, Harbor, Kafka 4.3 in KRaft mode, BaSyx, InfluxDB 3 Core, MediaMTX with Frigate NVR, Keycloak, Prometheus, Grafana and Loki | Open components the facility operates, patches and owns inside its own perimeter |
How much compute and hardware does AI in aviation and aerospace manufacturing need?
The compute follows from the models: the published aviation and aerospace manufacturing designs size it as follows, from no new hardware to a full GPU node.
| Design | Part | The design |
|---|---|---|
| Hangar Form Cell | Compute | A fanless industrial inference computer in the cell panel and an eight-card 48 GB PCIe inference server in the facility, joined one way across a hardware data diode; an OCP Time Card grandmaster orders every stream |
Is it cheaper to own AI hardware or rent cloud GPUs in aviation and aerospace manufacturing?
Each aviation and aerospace manufacturing design prices three years of ownership in its Appendix A, with every price cited, against renting the same capacity from a cloud region at its deepest three-year commitment where hardware is bought.
| Design | Line | Three years |
|---|---|---|
| Hangar Form Cell | Three-year cost | About 202,000 to 220,000 US dollars to own the priced classes over three years, the scanner and the server most of it; the two compute tiers alone cost about two fifths of the deepest three-year AWS commitment for eight L40S cards, which the offline requirement rules out anyway (Appendix A) |
What ontology or object model does an aviation and aerospace manufacturing AI system need?
The object model is the part that makes the system an ontology rather than a pipeline: typed objects for the things in the aviation and aerospace manufacturing operation, with properties, status values and typed links. Every published design ships its object model as hyper-ontology/1 JSON that loads into Hyper Ontology.
| Design | Size | Objects | Download |
|---|---|---|---|
| Hangar Form Cell | 14 objects, 14 links | Formed structural part, Sheet blank, Dieless cell, Robot arm, Forming stylus, Toolpath program, Part CAD model, Formability analysis, In-process metrology scan, Force trace, Work order, Nonconformance, Facility operator, Cell model service | objects.json |
Who approves the decisions an AI system makes in aviation and aerospace manufacturing?
In every published aviation and aerospace manufacturing design a named person makes the decision that changes the physical world; the system prepares it.
| Design | Human control |
|---|---|
| Hangar Form Cell | A named facility operator releases every program, an authorized person dispositions every nonconformance, and the certified safety function admits no software verdict as a control input |
Every answer on this page is drawn from the papers linked in it: each paper's At a glance table, model register and cost appendix. Full text for agents: llms-full.txt. Designed on Praxis; object models load into Hyper Ontology.