Integrated Computational Material Engineering Approach to Additive Manufacturing for Stainless Steel (316L)

The objective in this project is to implement and validate a probabilistic qualification framework that will enable additive manufacturing (AM) materials and part qualification through the use of a data-driven predictive model within a statistical framework. Senvol seeks to develop and validate a data-driven ICME probabilistic framework for assisting qualification of AM materials and parts. Phase II focuses on 4 main Thrust Areas: (1) Validation of the ICME probabilistic framework, (2) Extension quantification capability, (3) Data collection protocol development, and (4) Software improvement. The objective of the Phase II Base is to validate the probabilistic framework through a rigorous experimental program on a representative structural component, and implement a capability that would quantify the accuracy of extending previously trained ICME predictive models for use in predicting the ICME relationships of a new dataset. In the Phase II Option, the project team plans on demonstrating and validating an approach for using the predictive model, capabilities, and data collection protocol for the case where a new dataset is no longer accurately described by a previously trained model. This demonstration will show how to gather additional test data to re-qualify the updated process.