Integrated Computational Material Engineering Approach to Additive Manufacturing for Stainless Steel (316L)
Navy STTR 2016.A - Topic N16A-T022
ONR - Ms. Dusty Lang - email@example.com
Opens: January 11, 2016 - Closes: February 17, 2016
N16A-T022 TITLE: Integrated Computational Material Engineering Approach to Additive Manufacturing for Stainless Steel (316L)
TECHNOLOGY AREA(S): Ground/Sea Vehicles, Materials/Processes
ACQUISITION PROGRAM: EPE-17-03 Quality Metal Additive Manufacturing
OBJECTIVE: Develop an Integrated Computational Materials Engineering (ICME) approach to the Additive Manufacturing (AM) of stainless steel (316L), to predict final metal part quality and performance.
DESCRIPTION: Many Naval systems have long mean logistics delay times for expensive, limited production parts that are typically cast. There are part obsolescence issues with aging platforms, and diminishing sources of supply, which delay part production and increase part cost. NAVAIR and DLA have documented the 8-28 month cycle required to establish a new source of supply for limited production cast parts. Part studies have shown that establishing new AM parts to replace limited production cast parts is generally much faster and will have a cost advantaged when compared to obsolete or out of production cast parts, which currently hamper system readiness.
Integrated Computational Materials Engineering (ICME) provides the physics-based computational tools to quantify and link the interdependent Processing-Structure-Property-Performance relationships for materials. These tools tie the processes that produce parts to their material properties to ensure the design of the right material for an application. The application of ICME to the design of AM will help speed new materials and processes, where AM is appropriate, to reduce the time and cost of process/part qualification and certification. This will enhance operational availability and decrease total ownership cost for Navy systems. For this reason, ICME is a critical enabler in the Navy's AM Roadmap and implementation strategy.
For this project, the specific application is the design of additive manufacturing processes for stainless steel 316L aerospace parts using a powder-based approach with directed energy (laser or e-beam). The ICME tools must model, simulate, and predict part quality and performance based on input process parameters. This must include local composition, microstructure (including porosity and other defects), residual stresses and/or distortion, and mechanical properties. The intended use for these tools is to guiding part design, process development, and certification for use.
Modeling and simulation tools should be developed and validated to: predict production reliability; model accurately AM processes and part fabrication; quantify dimensional, microstructural, and mechanical property uncertainty; predict accurately residual stress and distortion; predict accurately number, percent, and location of defects, e.g., porosity; support selection of the optimal build strategy (energy, feed rate, path / hatch space); design of support structure; predict resultant microstructure; predict resultant material properties; assess part functionality based on key design features; provide a probabilistic framework to support rapid qualification or processes and parts; and establish and output upper and lower limits for key process parameters to ensure quality in process controls during later fabrication.
PHASE I: Determine the architecture for the ICME tool set, and define the existing and needed models to fill this architecture. Identify the existing thermo-physical and structure-property-processing datasets for 316L and map a plan for filling in the required data necessary for the toolset. Describe a framework for the subsequent qualification and certification of parts and processes using this ICME toolset.
PHASE II: Assemble and as necessary develop, and validate physics-based models to link additive manufacturing processing parameters to materials structure and subsequent properties. As necessary for computational efficiency, develop and validate surrogate models for these physics-based models. Develop a validated materials database needed to support these models and verify and validate the individual models. Demonstrate prediction of location-specific microstructure, defects, and properties (including predictions for variability) for a test geometry and set of processing instructions (that is, STL file, beam history profile, etc.) for a particular additive manufacturing system of choice by the development team. The technical metrics for ICME tools in Phase III are property prediction capability as a function of process and geometry: measured value is +/- 10% (T) and +/- 5% (O) of predicted ICME value with a confidence of 90%.
The successful project will provide an overall design tool architecture description with interface specifications for the necessary software tools and materials data. The documented software tool interface specifications will include component tool execution approach (for example, static linked subroutine, spawned mpi process, etc.), data I/O requirements and formats (for example, input list of five 64-bit integers representing in order , followed by five arrays of 64-bit IEEE floating-point data of size representing ), and message-passing methods (such as an ASCII data file named *fred.inp* formatted as ). The materials data specification will include a full list of the necessary materials data, including properties, error bounds and/or uncertainty, metadata requirements, and data and metadata format for use. The project reporting will include all data developed in this project in the specified formats.
PHASE III DUAL USE APPLICATIONS: Complete development of Integrated Computational Materials Engineering (ICME) process design tools that link interdependent AM Processing-Structure-Property-Performance relationships. Demonstrate the tool by designing processing approaches for component parts of industrial interest specified by the Navy Program Manager, and comparing critically the as-produced parts to the predictions using a combination of destructive testing and non-destructive inspection techniques. The technical metrics for ICME tools in Phase III are property prediction capability as a function of process and geometry: measured value is +/- 10% (T) and +/- 5% (O) of predicted ICME value with a confidence of 90%. Transition the final ICME process design tool to the Navy for its intended use. The material of interest, 316L stainless steel, is a common material for industrial applications requiring moderate strength and good corrosion resistance. It is also a significant material in the biomedical industry for surgical tools, and for implant applications. The agile precision manufacturing of specialty components from this material can lower the cost dramatically of these specialty items, and enable new commercial applications in high-reliability applications.
1. Frazier, William E. 2014. "Metal Additive Manufacturing: A Review". Journal of Materials Engineering and Performance. 23 (6): 1917-1928.
2. Mullins W.M., and Christodoulou J. 2013". ICME - Application of the revolution to titanium structures." Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, http://arc.aiaa.org/doi/abs/10.2514/6.2013-1848 accessed July 2015.
3. Horstemeyer, Mark F. 2012. Integrated computational materials engineering (ICME) for metals using multiscale modeling to invigorate engineering design with science. Hoboken, N.J.: TMS-Wiley. http://www.123library.org/book_details/?id=53355 accessed July 2015.
4. Integrated Computational Materials Engineering, National Materials Advisory Board Division on Engineering and Physical Sciences National Research Council, 2008, http://www.nae.edu/19582/Reports/25043.aspx, accessed July 2015.
KEYWORDS: Additive Manufacturing, ICME, materials engineering, stainless, modeling and simulation, physics, 316L
TPOC-1: William Mullins
TPOC-2: Billy Short
TPOC-3: Jennifer Wolk
Questions may also be submitted through DoD SBIR/STTR SITIS website.