AI-Generated CNC Programs — How to Verify Them Before Running

AI tools — from CAM-integrated assistants to general-purpose language models — can now generate G-code programs from natural language descriptions, CAD geometry, or machining parameters. The output looks like valid G-code, runs through a syntax check without errors, and loads onto the machine without complaint. None of that means it's safe to run.

AI-generated G-code introduces failure modes that differ from manually written programs and that require specific verification steps before the program reaches the control.

What AI Gets Right

AI tools trained on large G-code datasets handle standard operations reliably:

Basic syntax — G0, G1, G2/G3, G81–G84, M codes
Common program structure — safety line, tool call, G43, M30
Feed rate and spindle speed ranges for common materials
Standard canned cycle parameters

For simple programs — drilling a hole pattern, facing a flat surface, basic contour milling — AI output is often syntactically correct and structurally sound.

What AI Gets Wrong

Machine-specific behavior — AI has no knowledge of your specific machine's kinematic limits, parameter settings, controller firmware version, or tool changer requirements. A program that is syntactically valid for "a Fanuc controller" may not work correctly on your specific Fanuc 31i-B5 with a specific parameter configuration.

Post-processor logic — AI generates "generic" G-code, not post-processed output tailored to your machine. The difference matters for rigid tapping (M29 requirements), tool change sequences, and axis-specific modal codes.

Fixture and workholding awareness — AI knows the part geometry but not your fixture. Rapid moves that clear the stock may still collide with clamps, toe clamps, vise jaws, or step blocks.

Tool assembly geometry — AI assumes tool length and diameter but doesn't know your actual tool assembly — the combination of tool, holder, extension, and shrink fit that determines the real collision envelope.

Coordinate system assumptions — AI typically assumes G54 with the part origin at a corner or center, which may not match your actual setup.

Unit errors — AI output may mix mm and inch values, or generate inch feed rates for a machine configured in metric mode.

The Specific Risks of Language Model G-Code

General-purpose language models (ChatGPT, Claude, Gemini, and others) can generate G-code on request. Unlike CAM-integrated AI, language models have no access to your part geometry, machine specifications, or tooling data. The G-code they produce is based on pattern matching against training data — it looks plausible but may contain:

Incorrect feed rate calculations (wrong material or tool diameter assumed)
Plunge moves at rapid (G0) instead of feed (G1)
Missing G43 / tool length compensation
G28 in absolute mode (crash risk)
Incorrect canned cycle parameters for your controller dialect
Programs that exceed machine travel limits

Language model G-code should be treated as a starting point requiring full verification — not as a ready-to-run program.

Verification Checklist for AI-Generated Programs

Before running any AI-generated G-code, verify:

Syntax and structure:

Safety line present (G17 G21 G40 G49 G80 G90 or equivalent)
Correct work offset called (G54 or appropriate)
Tool length compensation active (G43 H_ on first Z move after each tool change)
G28 uses G91 (incremental), not G90
M30 or M02 at program end

Motion verification:

No G0 moves into the part (plunges should be G1)
R-planes in canned cycles are above the part surface
Z depths match the part design
Feed rates are appropriate for tool diameter and material

Machine-specific:

Canned cycle syntax matches your controller (M29 before G84 if required)
M codes are valid for your machine
Feed rates in correct units (mm/min vs inch/min)

Simulation:

Full machine simulation with actual fixture geometry
All tool change positions verified
No collisions with fixture, clamps, or machine structure

Why Simulation is Essential for AI Output

Manual G-code errors are typically localized — a programmer makes a mistake in a specific block and the error is usually close to the intended position. AI errors can be systemic — a wrong assumption propagates through the entire program (wrong units, wrong work offset, wrong tool length convention).

Running AI-generated G-code in a machine simulator before cutting catches systemic errors in the first pass. Eureka3X simulates the actual NC file on a full 3D machine model — the same environment the machine will execute. Systemic errors become visually obvious; a Z depth error that's off by a factor of 25.4 (mm/inch conversion) shows the tool plunging through the table rather than into the part.

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The Right Role for AI in CNC Programming

AI works best as an assistant, not an autonomous programmer:

Generate a program structure, then verify and edit
Suggest feed rates and speeds as a starting point, verify against tooling specs
Convert between controller dialects (Fanuc to Siemens syntax) with human review
Generate repetitive sections (bolt circles, arrays) that are tedious to write manually
Explain G-code blocks or controller-specific behavior

The human programmer retains responsibility for verifying that the final program is safe to run. Simulation is the verification layer between AI output and machine execution.