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

Period of Performance: 07/11/2016 - 05/10/2017

$80K

Phase 1 STTR

Recipient Firm

Scientific Forming Technologies Corp.
2545 Farmers Drive Suite 200 Array
Columbus, OH 43235
Firm POC
Principal Investigator

Research Institution

The Ohio State University
1330 Kinnear Road
Columbus, OH 43212
Institution POC

Abstract

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.