3D multiscale segmentation and morphological analysis of X-ray microtomography from cold-sprayed coatings

Area of Science:

• Materials Science
• Additive Manufacturing
• Imaging Techniques

Background:

• Cold-spray is an additive manufacturing process for creating coatings.
• Analyzing the microstructure of cold-sprayed coatings is crucial for understanding the deposition process.
• Existing imaging and analysis methods may struggle with noise and complex microstructures.

Purpose of the Study:

• To introduce a novel, noise-tolerant segmentation algorithm for analyzing X-ray microtomography data of cold-sprayed coatings.
• To provide new insights into the cold-spray deposition process through advanced image analysis.
• To validate the effectiveness of the proposed algorithm on real-world data.

Main Methods:

• Development of a noise-tolerant segmentation algorithm.
• Combination of deterministic multiscale segmentation and stochastic segmentation.
• Utilizing random Poisson lines as markers in the stochastic segmentation approach.
• Application and validation using X-ray microtomography data of aluminum particles.

Main Results:

• Successful application of the noise-tolerant segmentation algorithm to X-ray microtomography data.
• Demonstration of improved analysis capabilities for cold-sprayed coating microstructures.
• Validation of results on images of aluminum particles, confirming algorithm performance.

Conclusions:

• The proposed noise-tolerant segmentation algorithm offers enhanced analysis for X-ray microtomography of cold-sprayed coatings.
• This method provides valuable insights into the cold-spray deposition mechanism.
• The algorithm's robustness and accuracy are confirmed through experimental validation.