AI-Driven Machining: Building a Closed-Loop CNC System with IIoT Feedback

Author(s): R Rajesh Swamy

Originally published on Towards AI.

Every machining system begins with precision — but very few truly understand precision. Traditional CNC machines are masters of repetition: they cut, carve, and shape metal flawlessly, yet they remain blind to what’s happening at the tool tip. They follow instructions but don’t learn from them.

This project was built around a simple but powerful idea —

Can a CNC machine learn from its own behavior and become intelligent enough to improve itself?

That question led us on a long, data-driven journey — one that merged artificial intelligence, industrial IoT, and CNC automation into a unified, self-aware system.

We set out to build a Dental CNC platform capable of running autonomously from start to finish:

1. Take a 3D model (STL) of a dental part.
2. Automatically determine machining strategy using AI inference.
3. Generate optimized toolpaths using FreeCAD CLI.
4. Send GCode directly to a custom-built GRBL-based CNC.
5. Run multiple passes with real-time feedback from sensors.
6. Scan the completed part to generate a new STL.
7. Compare the finished mesh with the original and highlight differences.

All of this happens in a closed loop — where data from each cycle refines the next, making the machine smarter with every cut.

In the following sections, we’ll go through the complete end-to-end workflow, how the AI models were trained, how the CNC hardware was built from scratch, and how the inspection system closes the feedback loop.

Our test case:
A Dental CNC prototype — a compact, high-speed machining platform designed for sub-millimeter accuracy, powered by a neural understanding of its own operations.

The Complete Workflow

Before diving into the AI or hardware, it’s important to understand the overall structure of how data, automation, and machining connect together.

Our goal was to make the CNC system fully autonomous — capable of taking a 3D model and finishing the machining job without human intervention, while collecting live data at every stage.

This automation pipeline is built around three major stages:

Decision Stage — AI & Intelligence

This is where the system “thinks.”
It starts with the STL file and extracts key geometric and contextual parameters.
The AI model uses this data to decide:

• Which machining passes are needed (roughing, pocketing, outline, contour, etc.).
• What tool type to use (endmill, ballnose, bullnose, etc.).
• Optimal feed rates, step-down, and plunge depths.

The output is a machining meta profile, which drives the CAM generation.

Machine Stage — Execution & Telemetry

Here, the machining begins.
The generated GCodes are sent to the GRBL-based controller, which executes them layer by layer.
Sensors attached to the spindle and frame capture:

• Vibration (via IMU)
• RPM (via Hall sensor)
• Current (via Hall-effect sensor)

All this telemetry is streamed through an MQTT IIoT pipeline, forming the live data source for AI feedback.

Inspection Stage — Validation & Feedback

Once machining completes, we enter the inspection loop.
A 3D scan of the finished part is aligned with the original STL using Iterative Closest Point (ICP) registration.
A difference map is then computed, showing surface deviation color-coded by tolerance — providing immediate visual feedback on machining accuracy.

This difference mesh is both a quality validation tool and a training input for improving the AI model — ensuring that each cycle makes the system smarter.

Workflow Steps Overview

The complete workflow for AI-driven dental CNC machining can be summarized in eight steps:

1. Get Source STL — Obtain the base 3D model (e.g., dental part).
2. Extract Meta — Compute bounding box, height, depth, and length using Python + trimesh.
3. AI Decision — Use trained ML models to determine machining passes, tool type, and feed parameters.
4. Generate Pass GCodes — Run FreeCAD CLI scripts to generate multi-pass GCodes for each operation.
5. Send GCode to Machine — Stream using GRBL Streamer or bCNC.
6. Run Multi-Pass Machining — Execute all passes sequentially, collecting IIoT telemetry.
7. Scan Completed STL — Capture 3D scan of the final part.
8. Compare and Inspect — Align meshes, compute deviation, and visualize tolerance map.

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Published via Towards AI