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Application of Fractal Image Analysis by Scale-Space Filtering in Experimental Mechanics
Anna Bauer1, Wolfram Volk1, Christoph Hartmann1
1Chair of Metal Forming and Casting, Technical University of Munich, Walther-Meissner-Strasse 4, 85748 Garching, Germany.
Journal of Imaging
|September 22, 2022
Summary
Fractal analysis of optical data from shear cutting processes offers new ways to identify material parameters. This method enhances the accuracy of numerical analyses by providing more reliable input data.
Area of Science:
- Materials Science
- Fractal Geometry
- Optical Measurement Techniques
Background:
- Complex numerical analyses demand precise material parameters for realistic results.
- Experimental identification of material parameters is crucial for advancing scientific and engineering applications.
- Shear cutting processes generate complex data that requires advanced analysis methods.
Purpose of the Study:
- To investigate the application of fractal analysis to optical measurement data from shear cutting.
- To explore fractal analysis as a method for material parameter identification.
- To enhance the reliability and accuracy of input parameters for numerical simulations.
Main Methods:
- Fractal analysis based on modified scale-space filtering.
- Calculation of scale exponent fields from image sequences using a mobile microscope.
- Automated evaluation of local scale exponent values via least-square approximation.
- Digital image correlation to track changes in scale exponents for individual material points.
Main Results:
- Demonstrated the feasibility of applying fractal analysis to shear cutting optical data.
- Successfully calculated scale exponent fields characterizing the process.
- Quantified local scale exponent values using automated methods.
- Tracked variations in scale exponents of material points during the cutting process.
Conclusions:
- Fractal analysis provides a novel approach for material parameter identification in shear cutting.
- The integration of fractal analysis with digital image correlation offers a powerful tool for experimental mechanics.
- This methodology can improve the precision and reliability of numerical simulations in material science and engineering.
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