MIPAR: A Transformative Image Analysis Tool for Materials Research

Materials Science

Materials science and engineering refers to the two-pronged method of studying the different mechanical properties of advanced materials and applying that knowledge to address global challenges in manufacturing and technology. It represents a tandem effort between various interconnected schools of thought – biology, chemistry, physics, advanced mathematics, and much more.

At MIPAR Image Analysis, we have brought algorithmic image analysis into the materials science fold. With unmatched expertise in the field of custom algorithm development for complex image analysis, we can satisfy specific project objectives from nanoscale fibre thickness to metallic grain size measurements.

Grain Size Measurement, Particle Analysis & More

Micrograph particle analysis and materials characterisation are fundamental processes in most materials laboratories that support manufacturing, research and development (R&D), or quality assurance and control (QA/QC). MIPAR software eliminates the margin for error associated with classic micrograph analysis by automating the image capture process and building-in custom data extraction protocols based on a powerful deep learning toolbox. The result is an advanced, tailormade solution for fiber thickness analysis, grain size measurements, generic particle analysis, phase composition testing, and even defect assessments.

Browse a few example applications of our expert materials science software below. If you don’t see your sample type, simply contact a member of the MIPAR team today to discuss your needs.

Sinter metal grain size analysis following ASTM E112 standard. Fully automated grain size solution, analyze micrographs off of the microscope or run sample data sets in batch.
Metal powder particle size and shape characterization. Fully automated solution for quantifying particle size, roundness, and satellites. Analyze a single scan or batch a sample set.
Nanofiber porosity and thickness analysis. Quantify fiber density, void porosity and fiber thickness distribution.