Concepts

Deep Feature Extraction

How ARCNM turns CAD and drawings into typed features — geometry, PMI, GD&T.

Deep Feature Extraction is the first stage of every ARCNM quote: raw CAD bytes in, a typed, ID-stable feature graph out. Three layers work together:

3D feature recognition — semantic features on STEP / STP / STL / OBJ / IGES input.
Deep Drawing Understanding — extraction of PMI elements from drawings (PDF, image), at a depth chosen per drawing.
Fusion — binds 2D PMI elements (dimensions, tolerances, GD&T, surface finish) to the 3D features they constrain.

Every feature gets a deterministic UUID. Re-runs on the same input emit identical IDs so your downstream system can persist + diff.

The recognition layer is continuously refined from tenant-observed ground truth — every correction you submit feeds the model.

3D extraction

Input formats: .step, .stp, .stl, .obj, .iges.

Recovered output:

Output	Detail
Envelope	Bounding box, volume, surface area, centroid, principal axes
Wall thickness	Min / median / max
Features	Holes, pockets, faces, bosses, bends, slots, chamfers, threads, fillets, ribs, gears, splines
DFM issues	Undercuts, deep ratios, thin walls, sharp internal corners
Material classification	Metal / plastic / wood / composite + ISO machining group
PMI (semantic)	STEP AP242 tolerance + GD&T passthrough

Example response (3D-only)

{
  "envelope_mm": { "bbox": [120.0, 80.0, 25.0], "volume_mm3": 187500, "surface_mm2": 56400 },
  "wall_thickness_mm": { "min": 2.0, "median": 4.5, "max": 12.0 },
  "features": [
    {
      "id": "feat_a8e2…",
      "kind": "drilled_hole",
      "diameter_mm": 8.0,
      "depth_mm": 25.0,
      "through": true,
      "axis": [0, 0, 1],
      "face_id": "face_92ab…"
    },
    {
      "id": "feat_2d6f…",
      "kind": "pocket",
      "depth_mm": 6.5,
      "cross_section_mm2": 420.0,
      "open_faces": 1
    }
  ],
  "discipline": { "winner": "milling", "confidence": 0.94 },
  "material_classification": { "iso_group": "P", "candidate": "1.0577 — S355J2" }
}

Quote quality scales with the format you upload. STEP/STP yields a full semantic feature graph (typed holes, pockets, threads, tolerance zones). STL / OBJ are mesh formats — ARCNM still produces an envelope, DFM issues, and a discipline classification, but semantic features that depend on B-rep topology degrade gracefully.

Always upload the highest-fidelity format you have.

2D extraction

A single endpoint handles every drawing. ARCNM determines the right extraction strategy per drawing automatically — you don't pick.

What gets extracted

The typed DrawingExtraction payload covers:

Title block (part number, revision, scale, material, drawn-by, date)
General tolerances (ISO 2768, ISO 8015)
PMI supplements (every dimension + tolerance + GD&T frame)
Surface finishes (ISO 1302 / 21920 Ra/Rz callouts)
Edge conditions (ISO 13715 — burrs, sharps, radii)
Threads, chamfers, radii
Heat treatment, coating, NDT, weld callouts
Revision table, bill of materials, balloons
Sectional views, projection method, primary process

Every field is always present in the response (null or empty array when not on the drawing) so consumers never need to defensive- check key existence.

Pinning a tier

For most workloads, let ARCNM decide. For specific cases — compliance / audit runs where you need the highest-detail trail, or bulk-triage runs where you want the cheapest valid extraction — you can pin a tier on the request:

curl -X POST https://api.arcnm.io/api/v1/parts/calculations/quote \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "part_revision_id":      "1f…",
    "costing_environment_id":"7c…",
    "extraction_tier":       "full"
  }'

Valid values: "auto" (default), "lite", or "full". See Pricing.

Confidence and self-correction

Each extracted field carries a confidence score. Low-confidence fields trigger an automatic re-extraction with tighter constraints. The retry log lives in the audit blob under self_correction for inspection.

Fusion (binding 2D ↔ 3D)

Fusion binds 2D PMI elements to the 3D features they constrain. A diameter callout Ø8 +0.018 / -0.000 H7 on the drawing gets resolved to the specific drilled-hole feature on the 3D model, with its tolerance zone applied.

Output:

{
  "binding": {
    "feat_a8e2…": {
      "pmi_supplement_ids": ["pmi_d1…", "pmi_d2…"],
      "tolerances": { "diameter": { "fit": "H7", "upper_um": 18, "lower_um": 0 } },
      "surface_finish_id": "sf_b9…"
    }
  }
}

Unbound PMI (callout exists on the drawing but no 3D feature matches) stays in unbound_pmi[] for review — never silently dropped.

Stable IDs

Every feature, face, and PMI element gets a UUID derived deterministically from its geometric signature. The same STEP file run a year apart produces identical IDs. Persist them in your system, diff across revisions, drive change-management workflows.

The hash includes:

Feature kind + parametric signature (e.g. drilled_hole / Ø8 / depth 25 / through)
Bounding-volume centroid (rounded to 0.01 mm)
Adjacent-face fingerprint

Not in the hash:

Absolute position (so a part flipped in CAD space still matches)
Naming (CAD layer name doesn't affect matching)

Invariants you can rely on

Same input ⇒ same IDs. Always.
Feature graph is acyclic (a hole cannot reference its own parent).
PMI elements never lose source provenance — the audit trail records which page, which leader line, which source token.
Extraction is independent of calibration. Tuning your environment's cost coefficients never changes the geometry, features, or PMI we recognise.