Predictive Modeling Tools for Metal-Based Additive Manufacturing

Period of Performance: 02/17/2015 - 11/16/2015


Phase 1 SBIR

Recipient Firm

Sentient Science Corp.
672 Delaware Ave. Array
Buffalo, NY 14209
Firm POC
Principal Investigator


Project Summary/Abstract 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. Anticipated Benefits/Potential Commercial Applications of the Research or Development Sentients DCC technology will allow the additive manufacturing companies and related industries to design their components more efficiently and perform more accurate performance and life analysis. This specially is more significant when they use new materials in their design. This will significantly reduce the uncertainty and conservatism in design of new components and required expensive and time-consuming experimental testing, thereby improving design process, increasing performance, reliability and durability, and reducing cost of operation. The physical nature and computational strength of the developed predictive tool will help testing more geometries, materials and design concept resulting in better final products manufactured using AM processes. List of Maximum of 8 Key words that Describe the Project Additive manufacturing, high power computing (HPC), predictive tool, microstructure modeling, performance and life analysis, damage mechanics Summary for Members of Congress Additive manufacturing has increased over the past years. However, there is not a well-established standard for component qualification and industry relies on experimental testing. Sentients technology will reduce the design uncertainty of new components and expensive and time-consuming experimental testing, increasing performance, reliability and durability, and reducing operational costs.