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

We are proposing to identify an ICME architecture that will enable the multi-scale modeling of additive manufacturing (AM) process at both the component level as well as at the meso-scale level such that the final part quality and performance can be predicted accurately. At the component level, the proposed ICME framework would help in predicting residual stresses, distortion and the necessary support fixtures needed to minimize distortion, while considering optimal build conditions such as laser energy, the laser path and other relevant processing conditions. At the meso-scale level, the objective of the proposed ICME framework is to identify a computationally efficient methodology to predict local temperature distribution, molten pool shape, porosity and other relevant microstructural features. It is envisioned that the proposed ICME architecture would support surrogate models such as phenomenological models that can predict microstructural features as a function of processing parameters. By extension, the same ICME framework should be able to support surrogate microstructure to property models using either Neural network models or Bayesian models. Existing sensitivity analysis and probabilistic modeling techniques along with uncertainty quantification methods can be extended to model AM processes which would help in rapid qualification of additive manufacturing process and parts.