Integrated Computational Materials Engineering Success Stories and Cultural Barriers*

Transcription

1 Transformations Selected Works of G.B. Olson on Materials, Microstructure, and Design C.E. Campbell, M.V. Manuel, and W. Xiong, editors Copyright # 2017 ASM International W All rights reserved CHAPTER 21 Integrated Computational Materials Engineering Success Stories and Cultural Barriers* C.J. Kuehmann, QuesTek Innovations LLC G.B. Olson, Northwestern University and QuesTek Innovations LLC Introduction Several national academy studies of materials and manufacturing (Ref 1 4) have emphasized the unique opportunity of computational materials engineering. While the breadth of the 2008 ICME study (Ref 1) creates an impression of a field in its infancy, a 2004 study (Ref 2) uniquely charged with identifying best industrial practices for accelerating transition of materials and process technology identified practices which are now mature and ready for broader dissemination. We here update the state of best practices and consider the principal cultural barriers to Integrated Computational Material Engineering (ICME) proliferation. Computational Design Successes Flying Cyber Steel The first example of a commercial alloy created by computational design is the Ferrium C61 (AMS6517) high-durability gear steel now performing well in off-road racing applications (Ref 5). The first such designer alloy to reach flight qualification is the Ferrium S53 (AMS 5922) corrosion-resistant landing gear steel allowing a drop-in replacement for current nonstainless landing gear steels, eliminating the need for cadmium plating (Ref 6). S53 is a secondary-hardening steel strengthened by efficient M 2 C carbide precipitates and contains sufficient Cr content to provide passivation against general corrosion (Ref 7) as shown in the flow-block diagram in Fig. 1. The sequential processing steps experienced by the alloy are depicted on the left-most column of the flow-block diagram and are constrained to existing processes for steels employed in current structural aircraft applications to maximize manufacturability. The subcomponents of the alloy system are connected by process-structure and structure-property relationships, representing the mechanistic modeling tools necessary to employ quantitative computational design. The application of the system flow-block diagram in conjunction with the computational models begins with identification of key design trade-offs. Key to achieving the stated design *C.J. Kuehmann and G.B. Olson, ICME: Success Stories and Cultural Barriers, in Models, Databases, and Simulation Tools Needed for the Realization of Integrated Computational Materials Engineering, eds. S. Arnold and T. Wong, ASM International (2011), p 24 30

2 Chapter 21: Integrated Computational Materials Engineering Success Stories and Cultural Barriers / 403 goals is the development of an efficient strengthening dispersion. M 2 C carbide is an efficient strengthener in steels due to its high modulus misfit with BCC Fe and its ability to precipitate coherently at the nano scale. Models were utilized, as demonstrated in Fig. 2, to predict the overall precipitation driving force and the normalized coarsening rate constant of the M 2 C carbide as a function of the Mo and V content in the S53 alloy. The approach maximizes the resulting strength through the precipitation driving force, while assuring that strength can be achieved with reasonable tempering parameters. This design also maintains adequate martensite kinetics to ensure a lath martensitic alloy while achieving high strength as another key design trade-off. Quantitative martensite kinetic models (Ref 8) are used to predict the martensite start temperature along with the M 2 C precipitation driving force in Fig. 3 as a function of Co and Ni content. Fig. 1 The flow-block diagram for ultrahigh-strength corrosion-resistant steel indicates the desired property objectives, the microstructural subsystems, and sequential processing steps needed for design. Links between system blocks indicate quantitative models needed to effect the design via science-based computation. Fig. 2 Maximizing M 2C driving force while maintaining a normalized coarsening rate for adequate tempering kinetics provides a secondary-hardening alloy with highly efficient strengthening. The S53 alloy achieves greater than 1930 MPa tensile strength with only 0.2 wt% carbon. Fig. 3 Maintaining a sufficiently high M S temperature while maximizing carbide driving force allows efficient strengthening in a fully martensitic alloy. Contours of M 2 C precipitation driving force overlaid with the alloy M S temperature determine optimal Co and Ni content.

3 404 / Transformations Selected Works of G.B. Olson on Materials, Microstructure, and Design Fig. 4 One-dimensional multicomponent diffusion simulations of the solidification of S53 alloy indicate the extent of segregation that can be expected in final ingot. This segregation is validated by SEM observations of ingot material. Additional constraints on ductile fracture, grain boundary chemistry, and grain pinning dispersions are used to complete the design optimization and uniquely identify the alloy composition that represents the best compromise of the diverse design goals and constraints. Grain boundary chemistry considerations are applied based on first principles calculations of impurity and alloying effects on the interfacial cohesion of BCC low angle grain boundaries (Ref 9). The grain pinning dispersion design was based on calculation of the TiC/Fe interfacial adhesion that demonstrated excellent resistance to microvoid formation (Ref 10). Solidification simulations employing 1-D multicomponent diffusion calculations in DIC- TRA for candidate design compositions ensured production scale processability (Ref 11). Figure 4 demonstrates segregation profiles across secondary dendrite arms predicted for production scale ingots using VAR. These predictions were validated from actual production ingots. Figure 5 shows DICTRA predictions of homogenization Mo in the as-cast ingot of the S53 alloy. The simulation guides processing recommendations and indicates that sufficient homogenization can be achieved in production scenarios. Figure 6 demonstrates the validation of tensile properties in production ingots of up to 24,000 lb. size. Extending Computation Design to Qualification and Implementation Accelerated insertion of materials (AIM) methods (Ref 2) were applied to the prediction Fig. 5 The homogenization of Mo segregation in the S53 alloy as a function of time at 1350C. The two curves represent the composition of Mo at the dendrite center and edge, and, the initial profiles are provided by the solidification simulations from Fig. 4. of the ultimate tensile strength, a primary design objective of the S53 alloy. Prediction of property design minimums, which normally require 10 production heats of alloy and 300 individual observations, were completed using only three heats and 30 individual observations. A large simulated data set with over 300 simulations was produced using expected process variation and computational mechanistic models of alloy strength. The simulated property distribution was scaled to fit 20 experimental observations from two production heats by a linear transformation function determined by the best fit. This analysis indicated

4 Chapter 21: Integrated Computational Materials Engineering Success Stories and Cultural Barriers / 405 the 1% minimum ultimate tensile strength was predicted to be below the 1930 MPa design goal by at least 10 MPa. This early indication allowed additional process optimization to be completed, increasing the ultimate tensile strength by 10 MPa. The AIM analysis was repeated after a new temper was optimized, using 30 data produced from three heats of the alloy, and is shown by the solid curve of Fig. 7. The new property minimum estimate shows a prediction of 1930 MPa (280 ksi) meeting property objectives. The resulting full experimental data set of 10 heats and over 600 individual observations are represented by the square points of Fig. 7. The AIM prediction is within 7 MPa (1 ksi) of the 1% minimum tensile strength statistically defined by the 10 heats of data. In this AIM example, if data development had proceeded and the property deficit discovered when the full data set was complete, over a year of development and in excess of US $500,000 would have to be repeated to meet the property goals. The first flight of a Ferrium S53 landing gear occurred on December 17, Fig. 6 Validation of scale-up results after the computational optimization of homogenization parameters. The chart represents typical tensile properties for wrought product produced at three production scales, including laboratory 300 lb ingots, intermediate 3000 lb ingots and full production 24,000 lb ingots. The similar tensile properties across all products indicate the ingots are homogeneous across all production size scales. Integrated Computational Material Engineering Cultural Barriers Primary cultural barriers to the accelerated development and proliferation of ICME technology stem from the broader issue of the opposite nature of science and engineering. It has been Fig. 7 An AIM analysis of the ultimate tensile strength of S53 using simulation results and 30 data from 3 individual heats agrees well with the completed data set from 10 heats and over 600 observations in determining the 1% minimum design allowable.

5 406 / Transformations Selected Works of G.B. Olson on Materials, Microstructure, and Design much discussed that science seeks the discovery of new knowledge of nature as it is, while engineering applies existing knowledge to bring new technology into existence. The unique position of materials at the interface of science and engineering intensifies the inherent conflict between these opposite philosophies. With the glorification of basic science by the atom bomb, and the catalytic effect of Sputnik hysteria, it became temporarily fashionable for postwar American industry to invest directly in basic research. This created historic environments like Bell Labs (with achievements from this period still receiving Nobel Prizes) where science and engineering coexisted in an atmosphere of mutual respect and collaboration, enabling an unprecedented level of sciencebased technology creation. Concurrent with the development of MBA programs, major industry concluded that such basic research could not be justified in terms of short-term competitiveness, and largely dropped out of science. This in turn created a need for federally funded academic research to take full responsibility for basic science, with the unfortunate consequence that engineering at our research universities has been almost entirely replaced by engineering science. We are left with a system where universities do science and companies do engineering; probably the most egregious example of the muchdiscredited over-the-wall system. While limited technological competence at universities and limited scientific depth in industry offer an opportunity for unlimited finger pointing, the special case of university spinoff companies has provided the best exception to the prevalent systemic dysfunction, and it is no wonder that these have served as the principal source of the ICME methods, tools, and databases that are now available for broad dissemination. While a few elite engineering schools have attracted private funding to conduct actual engineering on campus, there is a pressing need for federal funding agencies to take responsibility for restoration of engineering across our research university system. DoD agencies have provided the best example so far, but far more impact could be achieved by the formation of a National Engineering Foundation as an equal and opposite partner to NSF, with a mission of direct technology creation through design as thecomplementofdiscovery.suchanenterprise would powerfully demonstrate societal respect for engineering as the equal of science, would provide a fertile intellectual environment far more capable of achieving the goals of Bayhe-Dole legislation in a technological return on our vast public investment in science, and would create an educational environment for engineering equaling that in place for science. REFERENCES 1. NRC 2008, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security 2. NRC 2004, Acceleration Technology Transition: Bridging the Valley of Death for Materials and Processes in Defense Systems 3. NRC 2003, Materials Research to Meet 21 st Century Defense Needs 4. NRC 2004, Retooling Manufacturing: Bridging Design, Materials and Production 5. C.J. Kuehmann, and G.B. Olson, GEAR- STEELS: Designed by Computer, Advanced Materials & Processes, 153, 5 (1998), C.J. Kuehmann, et al., Computational Design for Ultra High-Strength Alloy, Advanced Materials and Processes, 166 (2008), C.E. Campbell and G.B. Olson, Systems Design of High Performance Stainless Steels: I. Conceptual Design, J. Computer- Aided Mat. Des., 7 (2001), G. Ghosh and G.B. Olson, Kinetics of FCC BCC Heterogeneous Martensitic Nucleation, Part I: The Critical Driving Force for Athermal Nucleation, Acta Metall. Mater., 42 (1994), W.T. Geng, et al., Influence of Alloying Additions on Grain Boundary Cohesion of Transition Metals: First-Principles Determination and its Phenomenological Extension, Phys. Rev. B, 63 (2001) J.-H. Lee, et al., Strong Interface Adhesion in Fe/TiC, Philosophical Magazine, 85, 31 (2005), H.E. Lippard, et al., Microsegregation Behavior during Solidification and Homogenization of AerMet 100 W Steel, Met. Trans B, 29B (1998)