SBIR/STTR Award attributes
Fatigue life of parts produced by metal additive manufacturing is determined by the complex interaction of defects, surface properties, and material microstructure. Each of these constituents is affected by the choice of processing parameters, as well as the feedstock, machine performance, etc. In addition, stochastic events often contribute significantly to the fatigue life of individual samples or components. Incorporating all of this variability and complexity towards achieving the best fatigue life requires the use of machine learning and artificial intelligence to adapt to changing conditions and extend knowledge gained during expensive experimentation from part to part, machine to machine, and material to material. With this in mind, the proposed effort will demonstrate the feasibility of "smart" ICME-based AM optimization using machine learning of process feedback to produce components with predictable and enhanced fatigue life.
