Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning

Written by: by Stefan Bosse, Dirk Lehmhus and Sanjeev Kumar
Published on:

Summary

Detection and characterization of hidden defects, impurities, and damages in homogeneous materials like aluminum die casting materials, as well as composite materials like Fiber–Metal Laminates (FML), is still a challenge. This work discusses methods and challenges in data-driven modeling of automated damage and defect detectors using measured X-ray single- and multi-projection images. Three main issues are identified: Data and feature variance, data feature labeling (for supervised machine learning), and the missing ground truth. It will be shown that simulation of synthetic measuring data can deliver a ground truth dataset and accurate labeling for data-driven modeling, but it cannot be used directly to predict defects in manufacturing processes. Noise has a significant impact on the feature detection and will be discussed. Data-driven feature detectors are implemented with semantic pixel Convolutional Neural Networks. Experimental data are measured with different devices: A low-quality and low-cost (Low-Q) X-ray radiography, a typical industrial mid-quality X-ray radiography and Computed Tomography (CT) system, and a state-of-the-art high-quality μ-CT device. The goals of this work are the training of robust and generalized data-driven ML feature detectors with synthetic data only and the transition from CT to single-projection radiography imaging and analysis. Although, as the title implies, the primary task is pore characterization in aluminum high-pressure die-cast materials, but the methods and results are not limited to this use case.

Award

Best paper award for tomography outreach tools
Vidal, F. P., Afshari, S., Ahmed, S., Atkins, C., Béchet, É., Corbí Bellot, A., ... Tugwell-Allsup, J. (2024). Müller, B., & Wang, G. (Eds.). "X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development". Developments in X-Ray Tomography XV, SPIE Optics & Photonics 2024. DOI: 10.1117/12.3025315

Publications

  • Bosse, S., Lehmhus, D., & Kumar, S. (2024). Automated porosity characterization for aluminum die casting materials using X-ray radiography, synthetic X-ray data augmentation by simulation, and machine learning. Sensors, 24(9). doi:10.3390/s24092933
  • Vidal, F. P. et al (2024, Nov). X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development. Developments in X-Ray Tomography XV, SPIE Optics & Photonics 2024. doi:10.1117/12.3025315 .

Citations

@Article{s24092933,
  AUTHOR = {Bosse, Stefan and Lehmhus, Dirk and Kumar, Sanjeev},
  TITLE = {Automated Porosity Characterization for Aluminum Die Casting Materials 
    Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, 
    and Machine Learning},
  JOURNAL = {Sensors},
  VOLUME = 24,
  YEAR = 2024,
  MONTH = may,
  NUMBER = 9,
  ISSN = {1424-8220},
  DOI = {10.3390/s24092933}
}

@inproceedings{Vidal2024SPIE,
  author = {Franck P. Vidal and Shaghayegh Afshari and Sharif Ahmed and
    Carolyn Atkins and {\'E}ric B{\'e}chet and Alberto Corb{\'i} Bellot and
    Stefan Bosse and Younes Chahid and Cheng-Ying Chou and Robert Culver and
    Lewis Dixon and Johan Friemann and Amin Garbout and Cl{\'e}mentine Hatton and
    Audrey Henry and Christophe Leblanc and Alberto Leonardi and
    Jean Michel L{\'e}tang and Harry Lipscom and Tristan Manchester and
    Bas Meere and Simon Middleburgh and Iwan Mitchell and Liam Perera and
    Mart{\'i} Puig and Jenna Tugwell-Allsup},
  title = {{X-ray simulations with gVXR as a useful tool for education, 
    data analysis, set-up of CT scans, and scanner development}},
  volume = {13152},
  booktitle = {Developments in X-Ray Tomography XV},
  editor = {Bert M{\"u}ller and Ge Wang},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {131520W},
  keywords = {X-ray imaging, Computed tomography (CT), Simulation, 
    Digital twinning, Graphics processor unit (GPU) programming, 
    Image registration, Digitally reconstructed radiograph (DRR), 
    Machine learning},
  year = {2024},
  doi = {10.1117/12.3025315},
}