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FEA and Machine Learning Techniques for Hidden Structure Analysis.

Xijin Shi1, Sheng-Jen Hsieh1,2, Roseli Aparecida Francelin Romero3

  • 1Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA.

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Infrared imaging non-destructively predicts hidden plant root systems and internal bubbles in plexiglass. Machine learning models accurately analyze thermal data, offering a faster, cheaper alternative to traditional methods.

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Area of Science:

  • Materials Science
  • Plant Biology
  • Non-Destructive Testing

Background:

  • Traditional methods for analyzing hidden structures like plant roots or internal defects are often destructive, costly, or time-consuming.
  • Developing non-invasive techniques is crucial for efficient and accurate assessment in various scientific and industrial applications.

Purpose of the Study:

  • To investigate and predict two distinct hidden structures: plant root system architecture and non-visible bubbles within plexiglass.
  • To evaluate the efficacy of infrared imaging combined with machine learning for non-destructive analysis of these structures.

Main Methods:

  • Infrared imaging was employed to capture thermal data from plant root systems and plexiglass containing bubbles.
  • A finite element analysis (FEA) model simulated the infrared imaging process to generate thermal data for analysis.
  • Machine learning models, including polynomial regression, support vector machine (SVM), and artificial neural networks (ANNs), were developed to predict root structure, depth, and bubble characteristics.

Main Results:

  • The developed models provided valid predictions for both plant root system architecture and bubble dimensions (diameter and depth).
  • A line scan method, utilizing tree structure thermal profiles, successfully predicted main root branches.
  • Statistical tests confirmed the effectiveness and comparability of the predictive models.

Conclusions:

  • Infrared imaging coupled with machine learning offers a viable, non-destructive approach for characterizing hidden structures.
  • This integrated methodology presents a significant advancement over conventional destructive or time-intensive analysis techniques.