Fully Automated Microscope for Metallurgical Samples (Draft)

Automated microscopy systems are crucial for high-throughput characterization workflows, but commercial solutions are often too expensive and lack adaptability for research use. This paper presents an open-source, low-cost optical imaging system that automates sample visualization in microscopy or metrology contexts. The system integrates a motorized camera with adjustable lighting, a motorized platform for focus control, and a compact mechanical frame, all coordinated through a Python-based interface. Designed for deployment in a self-driving characterization lab, the system enables rapid pre-screening of metallurgical samples prior to SEM imaging. By identifying defective or unfit samples early in the workflow, the system reduces reliance on slower, higher-cost SEM scans, improving overall throughput and reducing wasted imaging time. Built entirely from off-the-shelf components and 3D-printable parts, the hardware is accessible to labs with limited resources and is fully documented for replication. This platform offers a robust, flexible solution for labs seeking to streamline sample throughput without the cost of commercial imaging systems.