06-08, 10:15–11:00 (Europe/London), Hardwick Hub
Additive Manufacturing (AM) enables complex, high-performance components, but porosity defects can compromise structural integrity. Traditional porosity analysis in X-ray CT scans is manual, slow, and inconsistent. This talk introduces a deep learning-based approach using CNNs and segmentation models to automate porosity detection, enhancing accuracy and efficiency. Attendees will gain insights into pre-processing 3D CT scans, training AI models, and solving industry challenges.
This talk delves into the application of deep learning to automate porosity detection in additive manufacturing (AM) components. Using convolutional neural networks (CNNs) and advanced image segmentation models, the session walks through the entire pipeline, from pre-processing 3D CT scan data to training and evaluating AI models, while addressing practical challenges like imbalanced datasets and computational costs.
As an informative and technical session, this talk demonstrates how AI can significantly enhance defect analysis, making quality control in AM faster, more accurate, and scalable. Attendees will leave with a clear understanding of the technical process, real-world applications, and the potential for AI to transform AM quality assurance.
Time Outline:
1. Introduction (0-5 min) – AM overview, porosity challenges, limitations of manual analysis.
2. Deep Learning for Porosity Detection (5-20 min) – CNNs, segmentation models, pre-processing.
3. Case Study (20-25 min) – Real-world application, performance metrics, challenges.
4. Future Directions (25-30 min) – AI-driven quality control.
This talk is ideal for AI practitioners, engineers, and researchers, bridging deep learning with industrial defect detection. While no hands-on activities are included, references to open-source tools and datasets will be provided for interested attendees that want to explore.
Previous knowledge expected
Onyekachukwu Ojumah is an AI Engineer with a strong background in data analytics, cloud computing, and machine learning. She holds an MSc in Artificial Intelligence from the University of Huddersfield and a BSc in Computer Science from McPherson University, where she graduated as the Best Graduating Student.
Currently, Onyekachukwu works as an AI Engineer at Victorian Plumbing, where she designs and implements AI-driven solutions to optimize operational processes and drive business innovation. She has co-authored research papers on AI applications across various industries, exploring how AI can transform workflows and decision-making.
As the organizer of PyData Huddersfield, she leads a vibrant community of data professionals and enthusiasts, fostering collaboration and knowledge-sharing around AI and machine learning. Onyekachukwu has also spoken at notable events, including DataFest Africa and MLOps Lagos, where she shared insights on AI-driven data engineering, model optimization, and data strategies.