Phase Analysis

• Automated Phase Identification

Traditional phase identification often requires experienced metallurgists to manually distinguish complex microstructures based on morphology, grayscale contrast, etching response, or compositional information. Our AI-powered platform automates this process using deep learning models trained on extensive metallographic and materials datasets. The system can automatically identify:

Even in challenging cases involving complex multiphase structures, non-uniform etching, low image contrast, irregular morphologies, and high-noise datasets.

• Intelligent Phase Quantification

Beyond phase recognition, the platform automatically performs quantitative analysis across the entire sample area. Capabilities include:

• Multiphase Segmentation and Interface Analysis

Modern engineering materials often contain highly complex multiphase structures where phase boundaries are difficult to distinguish manually. Our platform uses advanced semantic segmentation algorithms to automatically separate and analyze:

It can also evaluate phase connectivity, interface continuity, boundary morphology and local heterogeneity. These capabilities provide deeper insight into microstructural evolution and material behavior.

• Multimodal Data Fusion

Phase analysis becomes significantly more powerful when combined with compositional and crystallographic information. Our intelligent material analysis platform supports multimodal data fusion by integrating SEM morphology imaging, EDS elemental mapping, EBSD orientation analysis, XRD phase identification, optical microscopy datasets, SEM and automated metallography datasets. AI algorithms automatically correlate structural, compositional, and crystallographic information into a unified analytical framework.