With the end of the Cold War in 1989, plutonium production and processing in the U.S. was shut down. Around this time, the U.S. Department of Energy recognized the need for safe, long-time storage of surplus plutonium-bearing materials. In 1994, DOE issued the initial standard, DOE-STD-3013, titled "Stabilization, Packaging, and Storage of Plutonium-Bearing Materials," to provide criteria for preparing these materials for safe storage for up to 50 years. This standard was based on decades of experience with short-term storage and aimed to avoid issues like container degradation observed in earlier practices. It specified requirements for material stabilization (e.g., heating oxides to remove moisture), packaging in nested, welded stainless steel containers without organics, and limits on contents to avoid pressurization or corrosion. In 1995, DOE called for the construction of the Actinide Packaging and Storage Facility (APSF) at the Savannah River Site (SRS) in South Carolina to stabilize and package plutonium in compliance with the emerging standard. In 2001, DOE established the Integrated Surveillance Program (ISP), part of which is the Field Surveillance Program, that requires continuous destructive examinations (DE) of containers using statistical (99.95% probability criteria) sampling.

The DOE-STD-3013 storage system involves placing nuclear materials into a closed convenience can, which is placed into another slightly larger can that is welded shut, and then those two cans are placed into a larger third can that is also welded shut. The weld region in the second canister (the "Inner Canister Closure Weld Region") is the focus of much of the destructive testing. It involves emptying the canister, cutting it into sections, and imaging the inside of inspecting samples using various methods, such as a laser confocal microscope, wide area measurement system, scanning electron microscope, and X-ray computed tomography.

In 2018, Savannah River National Lab signed a contract with Computer Science and Engineering Professor Jason D. Bakos for what would become a $1.3M research project to develop a computer vision-based framework to assist staff at SRNL in identifying surface features in the scans that may indicate the presence of cracking and corrosion. This project seeks to develop an AI assistant that automatically identifies microscopic features for follow-up screening by a human inspector.