Additive Manufacturing: Alloy Design to Develop New Feedstock Materials: Session III
Sponsored by: TMS: Additive Manufacturing Committee, TMS: Alloy Phases Committee
Program Organizers: Joseph McKeown, Lawrence Livermore National Laboratory; Aurelien Perron, Lawrence Livermore National Laboratory; Manyalibo Matthews, Lawrence Livermore National Laboratory; Christian Leinenbach, Empa, Swiss Federal Laboratories for Materials Science and Technology; Peter Hosemann, University of California, Berkeley
Thursday 8:00 AM
November 5, 2020
Room: Virtual Meeting Room 4
Location: MS&T Virtual
Session Chair: Joseph McKeown, LLNL
8:00 AM Invited
Opportunities to Improve the Mechanical Properties of Titanium Alloys Produced by Laser Powder Bed Fusion: Marco Simonelli1; Graham McCartney1; Nesma Aboulkhair1; Yau Yau Tse2; Adam Clare1; Richard Hague1; 1University of Nottingham; 2Loughborough University
This talk presents the opportunities that laser powder-bed fusion (L-PBF) offers for the design and development of novel superior titanium alloys. From a detailed study of the solidification path of Ti-6Al-4V, our exemplar material, strategies to increase strength/ductility and address mechanical anisotropy in Ti alloys are proposed. Firstly, (i) the orientation relationship that links the low and high temperature phases (α and β, respectively) and (ii) the crystallographic defects introduced by L-PBF are utilised for prior-β grain refinement through epitaxial recrystallisation. The recrystallisation is characterised by high-temperature EBSD to inform on the early nucleation and growth of the β phase. The alloy design strategy used to develop a novel quaternary alloy Ti-6Al-4V-Fe with heat treatable metastable microstructure for increase in both strength and ductility is then presented. The merits of the design strategy and future outlooks and informed by high-temperature synchrotron X-ray diffraction, EBSD and TEM studies and mechanical testing.
8:30 AM Invited
Microstructure and Property Variability in DED Inconel 718 as a Function of Build Rate: Bernard Gaskey1; Ekta Jain1; Yong Chen Yeoh1; Guido Macchi2; Antonio Mattia Grande2; Matteo Seita1; 1Nanyang Technological University; 2Politecnico di Milano
Directed energy deposition (DED) is a candidate additive manufacturing technique for high volume production of metallic parts. The complex nature of thermal and material transport during DED, however, can yield significant microstructure heterogeneity and large property scatter which are difficult to eliminate using standard heat treatments. In this work, we investigate how the build rate impacts solute distribution and, consequently, the mechanical properties of Inconel 718. At high deposition rates, we measure differences in yield strength and hardness within the same build up to 15% and 10%, respectively. We interpret these differences in terms of the material cooling rate, which decreases along the build direction. Based on our results, we discuss possible strategies for microstructure and property homogenization of DED Inconel 718 without compromising on build rate.
9:00 AM
Micro-crack Mitigation by Alloy Modification in the Additively Manufactured Ni-base Superalloy CM247LC: Christian Leinenbach1; Seth Griffiths1; Hossein Tabasi2; Toni Ivas2; Xavier Maeder1; Anthony De Luca1; Kai Zweiacker1; Rafal Wrobel1; Jamasp Jhabvala2; Roland Logé2; 1Empa, Swiss Federal Laboratories for Materials Science and Technology; 2Ecole Polytechnique Fédérale de Lausanne (EPFL)
Additive Manufacturing (AM) of Nickel-base superalloys has the potential to create complex monolithic structures that can lead to performance and cost improvements in structures for high temperature environments. However, the rapid melting and solidification inherent to AM often results in micro-cracking. Samples were fabricated with CM247LC powder on two separate commercial SLM machines. Extensive SEM and TEM analysis was performed on the as fabricated samples to better understand microstructure, segregation, and defect formation. A combination of experimental data and thermodynamic simulations were utilized to develop a new variant of CM247LC with the goal to mitigate crack formation. The new alloy was processed with two commercial SLM machines and extensive microstructure characterization was performed. Other micro-crack mitigation strategies will be discussed.
9:20 AM
Processing of Y2O3-modified Nickel Superalloy by Selective Laser Melting.: Anthony De Luca1; Christoph Kenel2; David Dunand2; Christian Leinenbach1; 1EMPA; 2Northwestern University
The consolidation of Oxide Dispersion Strengthened (ODS) alloys usually involves pressing at high temperatures, which typically prevents the fabrication of complex-shaped parts. The machining of these materials is also diffi-cult. These limitations are thus preventing wider use of ODS materials. The viability of processing ODS powders by Selective Laser Melting (SLM) has been demonstrated in recent years on various materials (i.e. Nickel, Steels, TiAl..), allowing to produce parts with complex shapes, with limited dispersoid coarsening due to the short melt-pool lifetime and high cooling rate. The processability of a Y2O3-modified, γ'-strengthened Ni-Cr-Al-Ti model alloy was studied under various SLM conditions. We show how the molten metal interacts with oxide nanoparticles, and relate it to the resulting mi-crostructure (grain refinement, cracking susceptibility, porosity, slagging). Additionally, the efficacy of a HIPing procedure, as well as the interaction between the ODS nanoparticles and γ' precipitates, were investigated.
9:40 AM Cancelled
Development And Application of Thermodynamic Tools for AM Alloy Design: Aurelien Perron1; Bey Vrancken1; Nicholas Calta1; Tien Roehling1; John Roehling1; Thejaswi Tumkur Umanath1; Joel Berry1; Joseph McKeown1; Manyalibo Matthews1; 1Lawrence Livermore National Laboratory
Additive manufacturing (AM) technologies are revolutionizing not only modern component design but also materials design and evolution across many industries. Indeed, standard alloys are not necessarily suitable for rapid solidification processes like AM. However, new alloys cannot be designed easily due to complex interactions in multicomponent systems and the vast phase-space to be explored. Thus, a thermodynamic-based Materials Design Simulator (MDS) will be presented to develop new titanium-X alloys with optimal composition for microstructural control during AM. The implementation uses the Thermo-Calc® software coupled to an alloy optimizer. The MDS provides guidance to design new titanium alloys by searching for compositions with optimized thermodynamic criteria, while incorporating constraints (e.g., phase stability, composition boundaries) during and after solidification. Results for multi-component Ti-alloys with some experimental validations will be presented.Prepared by LLNL under Contract DE-AC52-07NA27344 and supported by the Laboratory Directed Research and Development Program under project tracking code 18-SI-003.
10:00 AM
Application of Taguchi, Response Surface, and Artificial Neural Networks for Rapid Optimization of Direct Metal Laser Sintering Process: Ebrahim Asadi1; Behzad Fotovvati1; Faridreza Attarzadeh1; 1University of Memphis
Direct metal laser sintering (DMLS) is a widely used powder bed fusion additive manufacturing technology that offers extensive capabilities to fabricate complex metallic components. However, this process has several variables (processing parameters), altering which increases the complexity of the correlations between them and the desired properties (responses) in order to optimize the responses. In this study, the influence of the most influential DMLS processing parameters, e.g., laser power, scan speed, hatch spacing, on relative density, microhardness, and various line and surface roughness parameters are thoroughly investigated. The significance of processing parameters on each response are analyzed using the Taguchi method. A multi-objective response surface method (RSM) model is developed for the optimization of DMLS processing parameters considering all the responses. Furthermore, an artificial neural network model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM method.