Optimization of Digital Image Correlation Patterning with Laser-Based Methods

Author(s):

Glen W. Cockcroft, Michael T. Hurst, Braden L. Miller, Daniel O. Lewis, and Brady G. Butler

Abstract:

This study covers the creation and optimization of a method for digital image correlation (DIC) analysis on niobium and copper substrates using a 20-W fiber laser. Initially, speckle size, contrast, density, and stochasticity parameters were identified for determining speckle pattern quality. Python scripts were developed to collect image data based on these parameters to be accurately scored for comparison. Vector and bitmap image patterns were then generated using Python code using placement masks and Poisson disc sampling methods, respectively. The vector patterns were ablated with 40% power and on copper substrates for compression testing with 3D DIC strain mapping. The bitmap patterns were ablated on a niobium surface polished at 800 grit and coated with black acrylic paint using 90% power and 15 loop counts for compression testing with 2D DIC mapping. The resulting strain maps revealed patterns capable of performing accurate and detailed tracking even under extreme deformation. In addition, pattern scores were compared with negative and positive control patterns using the Python script, providing evidence for the developed speckle quality. Overall, the results revealed high-quality patterns that were easily replicable using simplified methods.