1. ADDITIVE MANUFACTURING PROJECT CLUSTER MEETING NIST Measurement in Science & Additive Manufacturing Fundació CIM C/Llorens i Artigas, 12 Parc Tecnològic de Barcelona Barcelona, Spain 2 May 2016 Federico Sciammarella PhD, Northern Illinois University – America Makes member Jian Cao PhD, Northwestern University

2. Experimental & Numerical improvements in AM A comprehensive suite of integrated tools for process control and Additive Manufacturing part qualification Devise and test models that enable predictive process adjustments during the layer-by-layer, three-dimensional manufacturing method Guide development of customized engineered materials tailored to the capabilities of specific AM technologies, eliminating much trial-and-error testing

4. Exploitable results (ERs) from NIST MSAM New tool that is a calorimetry based system for monitoring/measuring and controlling heat input during metal AM. New tool that is an Acoustic Emission based to monitor powder flow in-situ for direct energy deposition systems Process-Structure- Property Modeling & Simulation tools for rapid qualification & certification

5. Calorimeter for monitoring/measuring and controlling heat input > Can measure Net Heat Input in Situ Enables real time closed loop process control > OEMs R&D facilities Service Bureaus > Repeatable Process Possible to target mechanical properties Possible to control mechanical properties

6. Calorimeter for monitoring/measuring and controlling heat input Technological Readiness Level TRL 4 Milestones in the TRL progression up to TRL9 Multiple locations to validate calorimeter and determine differences between systems Having an OEM perform beta testing on system prior t release Main technical challenges in this result Scalability to other platforms (i.e. powder bed) Time to market (Mth/yr):18-24 months IPRs – We have provisional patents in place we are hoping by next year to have paperwork on full patents

7. AE based in-situ powder flow monitoring system for direct energy deposition systems > Ability to monitor powder flow in-situ Better control during processing > OEMs R&D facilities Service Bureaus > Accurate control during deposition Minimization of creating defects Robust for industrial applications

8. AE based in-situ powder flow monitoring system for direct energy deposition systems Technological Readiness Level TRL 4 Milestones in the TRL progression up to TRL9 Calibration and robust algorithm development for multiple platforms Having an OEM perform beta testing on system prior t release Main technical challenges in this result Ensuring that algorithms developed are robust for various powder systems and types. Time to market (Mth/yr):18-24 months IPRs – We have provisional patents in place we are hoping by next year to have paperwork on full patents

9. Process-Structure- Property Modeling & Simulation tools for rapid qualification & certification In-house code couples thermal history with microstructure prediction Better T resolution Faster processing time > OEMS R&D facilities Universities > Rapid results High accuracy multiscale

10. Process-Structure- Property Modeling & Simulation tools for rapid qualification & certification Technological Readiness Level > TRL 4 Milestones in the TRL progression up to TRL9 Merge the platforms and create single source code Beta testing with materials database and experimental results across platofrms Main technical challenges in this result Extension of the Modeling Procedure to Incorporate Quantitative Concurrently Coupled Solidification Analysis Multiscale Volumetric Analysis Time to market (Mth/yr): 24-36 months Provisional patents have been applied and hoping to have full patents in next several years.

12. Project Example: Distortion Prediction Reduce the number of process iterations required to build a part using metal powder-bed AM through fast & accurate thermo- mechanical distortion prediction & pre-compensation of part geometry to mitigate distortion Accelerate the deployment of AM at large OEM’s and SME’s through reduced development cost & optimal material usage In-depth validation of 2 competing, high-speed, accurate process distortion prediction & compensation methodologies Total project value: Approx. $2.25M Project team: GE GRC, UTRC, Honeywell, Pan Computing, LLC, University of Louisville, Penn State University, CDI Corporation Timeline: August 2014 – August 2016

13. Expressions of Interest There are many interesting projects here and the main objective for us is to find like minded individuals that want to collaborate on improving process monitoring control and predictive models for rapid qualification & certification