🤖 Autonomous PCB Soldering Robot

A CNC precision 3-axis automated PCB soldering system with vision processing and AI solder recognition for in-app QA/QC. Built as a capstone project; awarded 1st place 🏆

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📋 Table of Contents

1. Overview
2. Key Features
3. System Architecture
4. Vision & ML
5. Documentation
6. Technology Stack
7. Results
8. Future Upgrades
9. Team
10. License

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📖 Overview

This project implements an autonomous PCB soldering robot that combines precision CNC motion control, computer vision, and deep learning to automate and verify solder joint quality. The system features a 3-axis CoreXY positioning system, custom electronics, and a MobileNetV2-based AI classifier that can detect good, bad, and missing solder joints in real-time.

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✨ Key Features

_{Fig 01. System modules: mechanics, electronics, firmware, vision, ML, and operator app.}

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🏭 System Architecture

The system integrates six major subsystems: mechanical positioning (CoreXY motion + Z-axis), custom electronics for motion control, firmware for kinematics and safety, computer vision pipeline, machine learning classification, and the operator GUI. Each subsystem is designed for modularity and independent development.

🔧 Mechanical (CoreXY + Z + feeder)

Rigid V-slot frame; 12 mm Y-rods for stiffness, 8 mm X-rods to reduce moving mass, GT2 belts on XY; Z by leadscrew. Toolhead integrates a temperature-controlled iron with a geared solder-wire feeder. A kitted tray fixtures boards repeatably.

_{Fig 02 — Toolhead CAD. Iron + geared feeder align wire at the pad; compact, low-mass carriage.}

_{Fig 03 — CoreXY CAD. Symmetric belt routing with idlers; X-rail on 8 mm rods.}

Additional fixtures

Fig 05 — Electronics enclosure. PSU + control PCB; thermal/EMI shielding for stable operation.

Fig 06 — Adjustable PCB tray. Hard-stop datums for repeatable imaging and soldering.

⚡ Electronics (motion, power, solder feed)

Custom controller: Raspberry Pi Pico + TMC2209 drivers on a single board; 24 Volt motion rail with 5 Volt buck for logic. UART access to driver current, microstepping, and diagnostics enables safe current limits and smooth feeds.

💾 Firmware (C++ / PlatformIO)

• CoreXY kinematics and soft/hard limit handling
• Variable motor speed (vs. variable steps) for stable ramps
• UART utilities for telemetry & tuning (foundation for future auto re-solder)
• Dynamic error handling with safety interlocks

axis_motion_960.mp4

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🔬 Vision & ML

vision_setup_960.mp4

📸 Acquisition & pre-processing

Lighting-controlled captures (Pi HQ + microscope lens + LED ring) → histogram equalization → BGR→HSV → Otsu threshold on Hue to isolate solder → V-channel gating to drop dim pixels → median filter → morphological cleanup → per-joint crops at 224×224.

Fig 09. Equalize → HSV → Otsu(H) → V-gating → median → morph → 224×224 crops.

Fig 10. Overlay of joint boxes with Good/Bad/Missing labels and confidences.

🏷️ Labeling & dataset

• Manual labeling in LabelImg with rectangular boxes around each solder joint; class set: Good, Bad (e.g., bridges/voids/insufficient wetting), Missing (pad present, solder absent).
• Augmentation: rotations, flips, Gaussian noise, slight blur, exposure jitter, and random spatter/dropout (~×7 expansion) to generalize across pad geometry and lighting.
• Splits: balanced train/val/test with class weights to mitigate skew; crops normalized before inference.

🧠 Classifier & training

• MobileNetV2 (α = 0.75) in TFLite for low-latency inference on Pi; trained on augmented crops.
• Loss: categorical cross-entropy with early stopping; metrics tracked per-class to ensure Missing stays separable from Bad.

_{Fig 11. Loss/accuracy vs. epochs; early stopping at convergence.}

_{Fig 12. Clear separation for Missing vs Bad.}

🖥️ Operator app

Tkinter GUI accepts camera / image / video inputs, renders detections with class/confidence, shows FPS, and writes CSV logs. Interface is structured to publish UART messages to the Pico for closed-loop re-solder in the next revision.

_{Fig 13. Batch assessment: per-joint labels & confidences across multiple boards; all decisions logged to CSV for QA and future closed-loop re-solder.}

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📚 Documentation

For comprehensive technical details, architecture decisions, design rationale, and implementation specifications, refer to the complete Capstone Final Report:

📄 S05 - Capstone Final Report.pdf

This 70+ page report covers:

• 📐 Complete mechanical design and CAD models
• 🔌 Detailed electronics schematics and PCB layout
• 💻 Firmware architecture and motion control algorithms
• 🧠 ML model development, training methodology, and evaluation
• 📊 Experimental results, testing procedures, and performance metrics
• 🔮 Future work and extension opportunities

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🛠️ Technology Stack

🤖 Hardware & Mechanical

• Motion Control: CoreXY belt-driven positioning system
• Actuators: NEMA 17 stepper motors with TMC2209 drivers
• Microcontroller: STM32H503 (ARM Cortex-M33)
• Vision System: Raspberry Pi 4 + HQ Camera Module
• Motion System: GT2 belts, linear rods (8mm/12mm), leadscrew Z-axis

💻 Firmware & Software

• Firmware: C++17 with PlatformIO
• Communications: UART telemetry for motor control
• GUI: Tkinter-based desktop application
• Computer Vision: OpenCV for image preprocessing

🧠 Machine Learning

• Model: MobileNetV2 (α = 0.75) with TensorFlow Lite
• Training: Two-phase fine-tuning with data augmentation
• Framework: TensorFlow/Keras
• Preprocessing: HSV color space, Otsu thresholding, morphological operations

🔧 Electronics

• Design Tool: Altium Designer for custom PCB
• Power Management: 24V motion rail, 5V buck converter
• Motor Drivers: TMC2209 with UART configuration
• Sensing: Endstops, limit switches, temperature monitoring

📊 Data & Labeling

• Labeling Tool: LabelImg for bounding box annotation
• Augmentation: Rotations, flips, noise, blur, exposure variations (~7x expansion)
• Dataset: Balanced train/val/test splits with class weighting

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✅ Results

Additional Highlights:

• ✅ Complete end-to-end pipeline from image capture to quality assessment
• ✅ Modular architecture enabling easy upgrades and modifications
• ✅ Production-ready GUI with exportable results for QA/QC workflows
• ✅ Comprehensive telemetry system for diagnostics and tuning

⏳ Future Possible Upgrades

• 🔄 Unify capture + inference on Picamera2 end-to-end
• 🔗 Close the loop: UART-triggered re-solder routines
• ⚡ Accelerate on Jetson/Coral; expand dataset with more boards/edge-cases
• 📊 Real-time analytics dashboard for production monitoring
• 🌐 Web-based interface for remote monitoring

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🤝 Team

Arji Thaiyib, Arjun Bhatia, Ahmad Ali, Abdullah Hafeez, Mayar Aljayoush
Supervisors: Dr. S. Shirani, Dr. C. Chen

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📄 License