GOALI: Investigation into a Microcutting and Planing-Induced Microfracture Based Non-Destructive Evaluation Technology of Super-Fast Measuring Material Fracture Toughness

About This Grant

Fracture mechanics has been studied for over a century, yet the rapid and accurate determination of fracture toughness through non-destructive methods remains a significant challenge. Current American Society for Testing and Materials (ASTM) standards rely on destructive testing of large specimens, such as compact tension samples with pre-cracked tips. These tests are costly, time-consuming, and often impractical for in-service infrastructure like pipelines, pressure vessels, and aircraft components. This Grant Opportunity for Academic Liaison with Industry (GOALI) project will investigate a novel approach based on microcutting and planing-induced microfracture to enable ultra-fast, minimally invasive toughness measurement. The method has the potential to transform asset management practices across industries by allowing safer and more efficient inspection of aging infrastructure, including bridges, ships, and vintage pipelines. The research will contribute to national health, prosperity, and welfare by advancing the state of non-destructive evaluation (NDE) technologies. Additionally, the project will support the education and training of graduate and undergraduate students, helping to build a skilled workforce in manufacturing and infrastructure sectors, while inspiring broader STEM engagement through outreach activities. The proposed method in this research project leverages a unique correlation between the cutting force and depth during microcutting, where the presence of a non-zero intercept in the force–depth diagram is indicative of material fracture toughness. This joint research effort between UCF and MMT will experimentally investigate this phenomenon, develop predictive models, and implement a testing protocol that does not require a pre-existing crack tip. The study looks to incorporate material microstructure, surface conditions, chemical composition, and service history to refine calibration accuracy using machine learning and statistical analysis. The resulting data seeks to bridge two major branches of fracture mechanics: traditional crack-tip based models and the emerging framework of 3D ductile fracture loci in uncracked bodies under multiaxial stress states. This integration intends to establish a robust theoretical foundation for a new generation of NDE fracture toughness testing technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.