Defects occurring in the energetic fills can alter a munitions safety and performance characteristics. Small defects, such as gaps, voids, cracks, and foreign materials, in the chemical makeup of the energetic material can lead to unforeseen chemical reactions that have lessened or increased the potency of the energetic material, that would lead to costly, hazardous, or even life-threatening misfires. Currently, energetic filled parts are manually inspected for critical defects during the manufacturing process using x-ray imagery. Manual assessment is not only costly, laborious, and time-consuming, but also prone to error and inconsistencies. In addition, x-ray imaging cannot adequately penetrate the metal shell surrounding the energetic material to show the condition of the chemicals within. Therefore, the Army has a critical need for accurate and reliable detection of energetic defects to eliminate defective parts from leaving the production floor, thereby reducing the potential for hazardous incidents in the field. To address this problem, Sky Park Labs (SPL) proposes EnergeticAI, an AI system that analyzes neutron radiographic images (n-rays) to automatically identify and localize energetic defects. Unlike X-ray radiography, neutron radiation can easily penetrate a high density metal containment such as the metal shell surrounding energetic material and interact with the lighter chemicals within. As a result, n-rays can image defects like cracks, voids, gaps, foreign materials in the energetic material that cannot be detected with x-ray, x-ray CT or any other non-destructive testing method. EnergeticAI uses weakly supervised machine learning to learn normal patterns exclusively from readily available, fault-free, n-ray images of munitions and highlights the location of deviations as potential defects. Then, the system automatically classifies the defect type and severity by clustering based on image similarity.
