Trusted to deliver excellence © 2015 Rolls-Royce plc and/or its subsidiaries The information in this document is the property of Rolls-Royce plc and/or its subsidiaries and may not be copied or communicated to a third party, or used for any purpose other than that for which it is supplied without the express written consent of Rolls-Royce plc and/or its subsidiaries. This information is given in good faith based upon the latest information available to Rolls-Royce plc and/or its subsidiaries, no warranty or representation is given concerning such information, which must not be taken as establishing any contractual or other commitment binding upon Rolls-Royce plc and/or its subsidiaries. Role of Process Modeling for Additive Manufacturing in Aerospace Jacque Bader Brandon Ribic

Additive manufacturing of metallic components 2 A fundamental understanding of these processes is necessary *Frazier, W. E. (2014). Metal Additive Manufacturing: A Review. Journal of Materials Engineering and Performance, Wire Feed Directed Energy Deposition* Powder Feed Directed Energy Deposition* Powder Bed Fusion* Fusion of successive layers of metal using a focused heat source which follows a pre-programmed path Each additive manufacturing process has unique characteristics and end uses Pre-programmed features of equipment limit broad application

Benefits of additive manufacturing aerospace components 3 Reducing the number of processing steps Reducing weight Increase fuel efficiency Expand function and application Reducing lead time Reducing life cycle costs

Additive manufacturing aerospace applications 4 Selective material addition Blades and vanes Case features Heat exchangers Fuel Nozzles Bearing housings Brackets Repair Elliott, A. (2013, Nov. 19). Additive Manufacturing. Retrieved 2015, from Oak Ridge National Laboratory: Withinlab.com

Why is additive manufacturing important to the aerospace industry? Production of geometries not possible with common manufacturing methods -Expand materials applications Enables production of functional light weight structures -Fuel efficiency, weight Consolidation of parts -Cost, weight, mechanical uniformity, waste, lead time Repair rather than remake -Cost Potential for competitive advantage 5 GKN.com AmericanMachinist.com

Repair of aerospace components via powder feed directed energy deposition 6 Thermal profile measurement is very difficult and may not be entirely accurate since molten pool is so small Microstructure is influenced by substrate cross section thickness, repair volume, and process parameters Need to achieve target geometry and mechanical properties Very difficult to be successful on first attempt Failure can result in costly rework or even scraping component Understanding the effects of repair geometry and process parameters on microstructure requires extensive experimentation airinsight.com

The dichotomy of additive manufacturing benefits 7 Design complexity makes post process inspection and qualification challenging Destructive inspection and certification can be costly For repair applications, destructive testing is not possible Qualifying complex components is costly and can require years of research Withinlab.com New tools and methods are needed to reduce qualification costs

Why is process modeling necessary? 8 Solving complex multi-objective problem in one process development program: -Time and cost -Many process variables affect resulting quality -Material challenges -Geometric constraints/effects -Monitoring/controlling challenges Measurement of molten pool temperatures, stress, and defect formation is difficult Development relies on many experimental iterations Repair applications often require near base metal properties Need to qualify many materials due to complexity of engine design 5+ years to develop and qualify each new component via additive manufacturing is not sustainable

Structure/process/property relationships The type of AM process, process parameters, component geometry, and tool paths/scan strategies affect cooling rate Cooling rate affects microstructure Multiple passes of the heat source can result in microstructure variation as a result of thermal cycling Many opportunities to introduce flaws into the component 9 Since microstructure affects mechanical properties and component life, process-structure-property relationships are important Base Metal Microstructure Microstructure of Deposited Material

What information can be calculated using process models? 10 Process modeling approximates process/structure/property relationships and can limit experimental development Temperature ProfilesFluid Flow Profile and Track GeometryResidual Stress and Distortion Phase Fractions Mechanical Properties Grain Morphology* *Lee, Y. S., Nordin, M., Babu, S. S., & Farson, D. F. (2014). Influence of Fluid Convection on Weld Pool Formation in Laser Cladding. Welding Journal, 292s-300s.

Additive manufacturing of metallic components consists of many complex physical processes 11 These physical processes are considered in the assumptions and algorithms of the process model Korner, C., Attar, E., & Heinl, P. (2011). Mesoscopic Simulation of Selective Beam Melting processes. Journal of Materials Processing Technology, Lee, Y. S., Nordin, M., Babu, S. S., & Farson, D. F. (2014). Influence of Fluid Convection on Weld Pool Formation in Laser Cladding. Welding Journal, 292s-300s. Laser Energy Absorption During Powder Feed AM Physical Processes During Powder Bed AM

Experimental validation of process models is critical to deriving benefit 12 Trusted modeling results come from extensive experimental validation -Temperature measurement -High speed video imaging -Metallographic evaluation -Residual stress measurement -Mechanical testing Important to recognize limitations to experimental validation methods Experimental validation can be challenging Identifying reliable and cost effective methods to validate process models is important

Determining preliminary parameters for material addition to a thin wall component 13 Laser powder feed directed energy deposition Analytical heat transfer calculation Multiple calculations performed Understand effects of process parameters on in molten pool geometry Evaluated a range of powers, travel speeds, and wall thicknesses Laser spot size and powder feed rate fixed Heat source traveling along thin wall component

Model provided insight to which parameters would cause negligible or excessive melting 14 Effects of laser power and travel speed not qualitatively surprising Understanding the quantitative critical values helped to limit test matrix Calculated temperatures and molten pool geometry useful to understand cooling rate and propensity for fine microstructures Melt Pool Mean Temperature in Kelvin Max Pool Mean Temperature in Kelvin Molten Pool Calculated WidthMolten Pool Calculated Length

Calculation of Track Geometry using a heat transfer and fluid flow model 15 Laser powder feed AM Three-dimensional temperature profile influences track geometry, microstructure and mechanical properties Track geometry is important to achieving target component geometry Evaluation of single tracks using various laser powers for material addition to IN-718 flat plate Lee, Y. S., Nordin, M., Babu, S. S., & Farson, D. F. (2014). Influence of Fluid Convection on Weld Pool Formation in Laser Cladding. Welding Journal, 292s-300s. Diagram of Model Solution Domain

Calculation of Track Geometry using a heat transfer and fluid flow model 16 Calculated track geometries were comparable with experimental results With a well validated model we can predict cooling rates which will allow for the prediction of residual stress, distortion, phase fractions and grain morphology Lee, Y. S., Nordin, M., Babu, S. S., & Farson, D. F. (2014). Influence of Fluid Convection on Weld Pool Formation in Laser Cladding. Welding Journal, 292s-300s. Comparison of Experimental and Calculated Track Geometries for Travel Speed of 24 in/min Models like this offer potential for process optimization while limiting the number of experimental trails

Is there more the scientific community can be doing? 17 Agreement and availability of material physical property data Continue to experimentally validate assumptions and models Identifying new methods of model validation User friendly models, which require limited experience and expertise Model run time is critical when basic experiments can be completed in minutes or hours. -Days may be too much time!! Developing models to prescribe process parameters and tool paths for a given material and component geometry Process optimization via integrated process models is highly desirable

Summary 18 The complexity of components produced by additive manufacturing gives rise to many benefits and challenges With operator ability to modify process parameters and tool path, the need for fundamental understanding becomes critical Process modeling provides fundamental process information which is otherwise difficult to experimentally measure Integrated process models can be useful for process optimization and the prescription of process parameters and tool paths As modeling capability improves, it will be important to maintain a user friendly interface and improve calculation efficiency Many thanks to: Ray Xu, Quinlan Shuck, Mark Nordin, Peter Daum, Andrea Meidell, Pavlo Earle, Amit Chatterjee, Bob Goetz, and many more.