Paul Harrison
My name is Paul Harrison, and I specialize in non-destructive testing (NDT) technologies for industrial applications. With a strong background in materials science and inspection engineering, I have dedicated my career to developing and applying advanced NDT methods to ensure the structural integrity and reliability of critical components across various sectors, including aerospace, energy, manufacturing, and infrastructure.
My expertise spans a range of techniques such as ultrasonic testing, radiographic inspection, magnetic particle testing, and eddy current evaluation. I have also been involved in projects that integrate AI and digital signal processing to enhance flaw detection, automate inspection workflows, and improve diagnostic accuracy in real time.
I am passionate about advancing NDT practices to meet the growing demand for safety, precision, and sustainability in modern engineering. By combining traditional inspection knowledge with emerging technologies, I aim to contribute to more intelligent, efficient, and cost-effective quality assurance systems.
Key features of this implementation:
Combines traditional image processing (OpenCV) with deep learning (TensorFlow/Keras) 12
Uses adaptive thresholding and morphological operations for defect segmentation 3
Implements contour detection with probability-based defect classification 2
Provides visual feedback with bounding boxes and probability scores 3
To use this code:
Replace 'ultrasonic_scan.jpg' with your input image path
Ensure you have the required libraries installed: pip install opencv-python numpy tensorflow
Train or obtain a pre-trained CNN model for ultrasonic defect detection 2
This implementation can be extended by:
Adding more sophisticated preprocessing techniques
Incorporating different NDT modalities (radiography, eddy current)
Implementing real-time processing capabilities
Adding classification for different defect types 1