Ultrasonic Thickness Measurement System (UTMS) Wiki - dbsandis/MyUTProject GitHub Wiki

Ultrasonic Thickness Measurement System (UTMS) Wiki

āœ… Project Summary āœ… Well-Organized Outline āœ… Detailed Section Breakdown āœ… Technical References & Contributions Guide

Overview

The Ultrasonic Thickness Measurement System (UTMS) is designed for recovery boiler pipe/tube inspections. It integrates a UT Data Logger, tracking software, and an analytical interface. This system enables precise measurement of material thickness using ultrasonic signals, making it valuable for industrial and structural monitoring.

Table of Contents

  1. Project Introduction
  2. System Architecture
  3. Hardware Components
  4. Software and Data Processing
  5. PCB Design & Fabrication
  6. Assembly & Testing
  7. Microcontroller Selection
  8. Deployment & Future Enhancements
  9. References

Project Introduction

The UTMS leverages ultrasonic technology to measure thickness and detect potential defects in pipes, boiler tubes, and industrial components. This system is designed with:

  • A single-board computing solution (Compute Module 5)
  • Custom ultrasonic amplifier and ADC
  • Real-time data processing and visualization

System Architecture

Block Diagram

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Key Modules

  1. Compute Module 5 (CM5)

    • Controls ultrasonic pulsing and data acquisition.
    • Interfaces with the high-speed ADC via SPI.

  2. Ultrasonic Pulser & Receiver

    • Generates high-voltage pulses for the transducer.
    • Amplifies received echoes for processing.

  3. Analog-to-Digital Converter (ADC)

    • Converts analog ultrasonic echoes into digital signals.
    • Interfaces with the Compute Module for processing.

  4. Graphical User Interface (GUI)

    • Visualizes thickness measurements and defect detection.

Hardware Components

1. Compute Module 5 (CM5)

  • Compact single-board computer for processing and control.
  • Interfaces with ADC, pulser circuit, and GUI.

2. Ultrasonic Transducer

  • A 5 MHz probe for industrial-grade thickness measurement.
  • Requires a BNC connector for interfacing.

3. High-Speed ADC

  • ADS127L01 (24-bit, high-speed ADC).
  • Enables real-time sampling of ultrasonic echoes.

4. Ultrasonic Pulser

  • TC6320 & TC2320 generate 50Vā€“500V pulses.
  • High-voltage switching using MOSFETs & PIN diodes.

5. Receiver Circuit

  • Low-noise amplifier (AD8429, AD620) enhances weak signals.
  • Bandpass filter (5 MHz) removes noise.

6. Power Management

  • 5V / 3.3V regulated supply for Compute Module 5.
  • High-voltage supply (50V-500V) for the pulser.

Software and Data Processing

1. Signal Processing Algorithms

  • Time-of-Flight (ToF) Calculation: [ Thickness = \frac{Velocity \times ToF}{2} ]
  • Fast Fourier Transform (FFT) for frequency-domain analysis.
  • Bandpass Filtering (5 MHz) to remove unwanted noise.

2. Compute Module 5 Codebase

  • Python / C++ for SPI communication and data processing.
  • RPi.GPIO / pigpio for GPIO control.

3. Data Visualization

  • Matplotlib / OpenCV for waveform plotting.
  • Flask/Django for web-based dashboards.

PCB Design & Fabrication

1. Schematic Design (KiCad)

  • PCB layout includes Compute Module 5 interface, ADC, and pulser.
  • Power rails (5V, 3.3V, HV) for efficient operation.

2. Fabrication Services

  • JLCPCB / PCBWay for low-cost manufacturing.
  • OSH Park for small-batch prototyping.

3. Assembly

  • SMD assembly (JLCPCB) or manual soldering.

Assembly & Testing

1. Hardware Testing

  • Oscilloscope for pulse & echo waveform verification.
  • Multimeter for voltage measurements.
  • Logic Analyzer for SPI communication debugging.

2. Signal Calibration

  • Test with known material thicknesses (steel, aluminum).
  • Adjust amplifier gain and filtering for optimal signal detection.

Microcontroller Selection

Best Options for Real-Time Processing

MCU Best Use Case Pros Cons
STM32H7 Real-time signal processing Fast ADC, DSP support No built-in Wi-Fi
ESP32-S3 Wireless IoT-based thickness gauge Wi-Fi, Bluetooth Slower ADC
RPi Pico (RP2040) Low-cost, compact PIO for custom I/O Needs external ADC
BeagleBone Black Advanced processing PRU for real-time tasks Higher power consumption

Deployment & Future Enhancements

1. Possible Enhancements

  • AI-based defect detection using TensorFlow.
  • Bluetooth/Wi-Fi integration for mobile apps.
  • Battery-powered portable version.

2. Open-Source Contributions

  • GitHub Repository for Code & Hardware.
  • Community Support & Wiki Documentation.

References

  1. Compute Module 5 Datasheet
  2. ADS127L01 ADC Datasheet
  3. KiCad PCB Design Guide
  4. JLCPCB Fabrication
  5. Python Ultrasonic Signal Processing

How to Contribute

  1. Fork the GitHub Repository.
  2. Submit Pull Requests for Enhancements.
  3. Report Issues & Improvements on GitHub.
  4. Discuss & Collaborate via GitHub Discussions.

This Wiki is actively maintained. Feel free to contribute with improvements, test cases, and hardware modifications.