Materials Science Issues

Machine learning-based particle size analysis for various materials.

We offer fully automated, AI-powered quality assurance and material characterization, ranging from residual analysis with VDA 19-compliant reporting to advanced quantitative microstructural analyses. Integrated calibration ensures measurement accuracy, while large-area, high-throughput analyses efficiently capture process effects, microstructural homogeneity, and pore distributions across multiple samples. Using a “tiling” approach, we offer field- and region-specific analyses, including the visualization of larger defects, fine geometric evaluations, and grain size determination.

AI-powered workflows encompass semi-automated measurements of geometric parameters and the automated quantification of microstructural features such as grains, phases, or defects, which can be integrated into SQL databases to enable reproducible data management, supported by the automated generation of custom reports to assist with audits. By combining AI, automation, high-throughput analysis, and data-driven analytics, our solutions deliver highly accurate, reproducible insights that accelerate quality assurance, process optimization, and materials research for a variety of materials.