Predictive Modeling Tools for Metal-Based Additive Manufacturing

Sentient proposes to develop predictive modeling tools for parts made through the additive manufacturing (AM) processes. Our approach requires the use of high performance computing (HPC). The use of additive manufacturing processes to make different engineering components has been increased over the past years. However, there is not a well-established standard for qualifications of these components and industry relies mainly on experimental testing for qualification purposes and behavior analysis of these components. Therefore, in order to obtain a reliable performance and a life prediction model, a physics-based model is needed to analyze the microstructure of these components and reliably predict their performance. During Phase I, Sentient is proposing to incorporate its DigitalClone-Component (DCC) modeling tool to develop modeling software that includes the microstructural features of AM materials and components manufactured from, and use the developed model for their performance analysis and life prediction. The different steps of this model are computationally expensive and use HPC. This model not only accounts for the effect of microstructure on the performance of AM components, but also predicts their fatigue life where currently the experimental testing is heavily used. In Phase II, we will implement our improved model for performance analysis of more complex geometries and inclusion of in situ adjustments.