py2DIC

py2DIC computes two-dimensional displacements and strains from digital images using template matching for 2D Digital Image Correlation (DIC) to enable non-contact optical measurement of material deformation.

Key Features:

• Template matching algorithm: Uses template matching to quantify local image shifts between reference and deformed frames.
• 2D displacement and strain computation: Computes two-dimensional displacements and derives strain fields from image sequences of samples under mechanical loading.
• High-resolution image input: Operates on high-resolution digital camera images for non-contact optical measurements of deformation.
• Python implementation: Implemented in Python.
• Validation against established tools: Processing of experimental datasets was compared to Ncorr, Vic-2D, DICe and to measurements from a strain gauge for accuracy assessment.

Scientific Applications:

• Experimental mechanics: Measures displacements and strains to analyze material response under various loading conditions.
• Model validation and material design: Provides quantitative deformation data to validate theoretical models and inform material design and improvement.
• Failure and deformation analysis: Characterizes strain distributions and deformation mechanisms for material science and mechanical engineering research.

Methodology:

Implemented in Python and employing a template matching method to compute two-dimensional displacements and strains; validated by processing experimental datasets and comparing results to Ncorr, Vic-2D, DICe and a strain gauge.