SBIR/STTR Award attributes
Additive manufacturing (AM) technology offers the potential to fabricate complex geometries that cannot be realized using conventional subtractive methods. In current industrial AM processes, determination of the type and distribution of support structure is often based solely on technician experience. This process is far from optimal and as a result, significant non-recurring engineering costs are incurred to develop a repeatable AM fabrication process for production. A method for optimizing support structure to reduce distortion is necessary to reduce build costs and improve build quality; two issues that have limited the proliferation of AM processes in industry. Currently, a computational tool that is capable of optimizing the support structure design to minimize thermally-induced residual stress or distortion does not exist. The key challenge to the development of an efficient tool for support optimization was the lack of an ultra-fast method for predicting residual stress and distortion in a part. In Phase I, Materials Sciences Corporation teamed with the University of Pittsburgh developed a fast AM process simulation model that runs in only a matter of minutes for a realistic part. In Phase II, the team will use this model to develop a support optimization tool for integration into commercial support-generation software.
