Scanning Laser Epitaxy (SLE) – Technology for Additive Repair and Manufacturing of Turbine Engine Hot Section Components Suman Das Professor and Director Direct Digital Manufacturing Laboratory Woodruff School of Mechanical Engineering & Georgia Tech Manufacturing Institute Georgia Institute of Technology Atlanta, Georgia GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY DIRECT PART MANUFACTURING DEFINING THEMES IN GEORGIA TECH’s DDM LAB Direct Part Manufacturing and Remanufacturing of Turbine Engine Components Large Area Maskless Photopolymerization (LAMP) Digital 3-D printing of ceramic cores and integral cored molds. Disrupts conventional investment casting. Foundry compatible. No change to conventional metallurgy. Eliminates variation. Enables advanced cooling designs. Enables breakthrough efficiencies. GEORGIA INSTITUTE OF TECHNOLOGY Scanning Laser Epitaxy (SLE) Disruptive direct metal additive manufacturing and repair process. Processing of non-weldable, and non-repairable hot-section nickel-base superalloys. Repair and refurbish fielded engine components to “as good as new” or “better than new”. Produce functional fully dense, 3-D components from metal powders. Enables surge production. DIRECT DIGITAL MANUFACTURING LABORATORY DIRECT PART MANUFACTURING PROJECTS & SPONSORS Cyber-Enabled Direct Digital Manufacturing of Heterogeneous Multifunctional Components (DDM of HMCs) – ONR Program Manager: Dr. Khershed Cooper Scanning Laser Epitaxy of Single-Crystal Nickel Superalloys – ONR Program Managers: Dr. Khershed Cooper and Dr. Ralph Wachter Direct Digital Laser Manufacturing of Nickel Superalloy Single-Crystal Components – ONR Program Managers: Dr. Ralph Wachter and Dr. Khershed Cooper DURIP: High Power Fiber Laser for Direct Digital Manufacturing – ONR Program Manager: Dr. Khershed Cooper Direct Digital Manufacturing of Airfoils – DARPA, Defense Sciences Office Program Manager: Dr. Bill Coblenz GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Additive Repair and Manufacturing of Hot Section Turbine Engine Components Through Scanning Laser Epitaxy (SLE) GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY TEAM Dr. Khershed Cooper Program Manager Dr. Suman Das (PI) Rohan Bansal Ranadip Acharya Justin J. Gambone Paul Cilino Office of Naval Research CYBER ENABLED MANUFACTURING SYSTEMS (CeMS) GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Science and Technology Development Goals • Develop thermally-controlled laser additive manufacturing (AM) of nickel-base superalloys for the repair of turbine engine hot-section hardware. • Develop AM methods for nickel-base superalloy components with predictable and controlled microstructure directly from powder feedstock for small lot functional prototyping and for surge production. • Investigate solidification control during laserbased additive manufacturing (AM) of nickel-base superalloys and other high temperature materials, particularly of hot-section alloys. • Establish process physics understanding for controlling the solidification microstructure as a function of process parameters with predictive models, sensors and real-time process control . GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Motivation – DoD Repair Needs Applications: Repair Needs for In-service Superalloy Turbine Hardware AV-8B Harrier Rolls-Royce F-402 Pegasus Engine CMSX-4 single crystal turbine blades V-22 Osprey Rolls-Royce AE1107 Engine Hercules C-130J Rolls-Royce AE2100 Engine E-2C Hawkeye Rolls-Royce T56 Engine CMSX-4 single crystal turbine blades GEORGIA INSTITUTE OF TECHNOLOGY Apache AH-64 GE T700 Engine DIRECT DIGITAL MANUFACTURING LABORATORY Motivation – DoD Surge Production Needs • Surge Production of turbine airfoils for long-range standoff weapons (LRSO) through additive manufacturing. • Engine OEM studies indicate up to 60% cost reduction relative to investment casting in surge production scenario. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SLE Application Development Approach Additive Repair of Turbine Components – Develop repair technology for nonweldable or non-joinable superalloys including equiaxed, DS and SX. – Repair of manufacturing defects on surfaces: Porosity, hot tearing, inclusions, misruns. – Repair of aging stationary and rotating engine components. Additive Manufacturing of Turbine Components – Apply the learnings from repair to metal-powder bed-based layer-by-layer 3-D additive manufacturing. – Produce fully dense, defect free components without tooling or post-processing. – Process advanced material systems considered extremely difficult or expensive conventionally. – Produce functionally graded components that are currently difficult to manufacture. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Processing Challenges for Hot Section Alloys • Solidification Cracking/Hot tearing. • Grain Boundary Liquation Cracking. • Strain Age Cracking. • Recrystallization and Loss of Underlying Microstructure. • Preserving nominal alloy composition. • Tramp elements in alloy composition. • Oxidic cleanliness and impact on properties. • Powder contamination. • Unmelted powder particles trapped in the melt. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Processing Challenges for Hot Section Alloys Area of interest GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Scanning Laser Epitaxy (SLE) •Process enabling direct additive manufacturing of three-dimensional objects with specified microstructure. •Based on controlled melting and resolidification of a pre-placed metal powder on a base substrate of same or similar alloy composition. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Scanning Laser Epitaxy (SLE) • • • Tightly focused scanning laser’s rapid movement induces surface tension gradients and incorporates powder into the melt pool Partial remelting of underlying substrate to initiate nucleation off prior grains. Epitaxial solidification through heterogeneous nucleation. Galvanometer scanners Stroboscopic video microscope Fiber laser beam Laser beams Laser Head Reflected radiation Focusing optic Pyrometer Power control signal Xenon strobe Analog voltage output Data acquisition Processing and controller Probe beams Laser Power supply Solidification microstructure control as a function of laser processing parameters and thermal boundary conditions on deposition substrate. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY TECHNICAL APPROACH GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SLE ALLOY PROCESS DEVELOPMENT • Demonstrate SLE of Ti-6Al-4V Demonstrate SLE of Equiaxed MARM-247 Demonstrate SLE of DS MARM-247 Demonstrate SLE of SX CMSX-4 Demonstrate SLE of Rene 80 SLE Development of Other Equiaxed, DS and SX Alloys • IN 100 • GTD 111 • PWA 1480 • PWA 1484 SX • IN 738 • IN 738LC • CM247LC • Rene 108 EQ • Rene 108 DS • Rene 220 • TiAl GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SCANNING LASER EPITAXY OF MARM-247 SOME EXPERIMENTAL RESULTS BB 10mm MARM-247 deposited on Cast MARM-247 plate AA Fully dense, crack-free and pore free microstructure of MARM-247 deposit • Fully dense deposits produced • No cracking upon stress relief heat treatment at 1067 C GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SCANNING LASER EPITAXY OF MARM-247 SOME EXPERIMENTAL RESULTS BB CC AA MARM 247 deposit 1 mm MARM 247 deposit Bond line Bond line MARM 247 plate substrate GEORGIA INSTITUTE OF TECHNOLOGY MARM 247 plate substrate DIRECT DIGITAL MANUFACTURING LABORATORY SCANNING LASER EPITAXY OF MARM-247 and CMSX-4 SOME EXPERIMENTAL RESULTS AA 2 mm CMSX-4 deposited on Cast CMSX-4 plate AA Fully dense, crack-free and pore free single-crystal microstructure of CMSX-4 deposit GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SCANNING LASER EPITAXY OF CMSX-4 EBSD CHARACTERIZATION RESULTS AA Upper Figure: Optical micrograph along cross-section AA of CMSX-4 deposit Lower Figure: EBSD orientation color map showing growth orientation of deposited material in good agreement with parent structure. AA 2 mm GEORGIA INSTITUTE OF TECHNOLOGY Top View of CMSX-4 deposit on CMSX-4 subatrate. Section AA is shown in red. Inverse Pole color map Figure DIRECT DIGITAL MANUFACTURING LABORATORY SCANNING LASER EPITAXY OF RENE 80 SOME EXPERIMENTAL RESULTS Fully dense, crack-free and pore free microstructure of Rene 80 deposited on Rene 80 plate, as processed Fully dense, crack-free and pore free microstructure of Rene 80 deposited on Rene 80 plate, post heat treatment GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Process Modeling Key Modeling Needs • • • • • • • • • Accurate thermal modeling and melt depth prediction to better control the process. Microstructure evolution modeling as a function of processing parameters and solidification conditions. Prediction of the onset of microstructural transformations including CET and OMT as a function of processing parameters for DS and SX alloys. Modeling of thermal stresses. Hot tearing criteria to predict crack formation during processing. Modeling of phase transformations during solidification. Development of microstructure-processing-property maps. Models for accurate modeling of process physics. Models for real-time process control. DIRECT DIGITAL MANUFACTURING LABORATORY GEORGIA INSTITUTE OF TECHNOLOGY Review of Prior Modeling Work • Rosenthal Solution was used by several researchers. • FEM approach was used to solve the conduction based model for LENS, Laser Cladding processes. • CFD simulation is used to incorporate melt pool convection as well as Marangoni convection. Gaps Identified • Simulations in prior work are all based on a single-bead approach and lack the generalization required to incorporate the raster scanning and associated microstructure modeling in the SLE melt pool. • Prior works do not incorporate local property variations with temperature. These can change the simulation results significantly while providing greater accuracy. • Time tracking of the melt pool position is required for raster scan approach M. Gaumann, et. Al., Acat Materialia 49, pp.1051 Childs, T.H.C. et al, CIRP Annals - Manufacturing Technology, 2004. 53(1): p. 191-194. Liu, W. and J.N. DuPont, Acta Materialia, 2004. 52(16): p. 4833-4847. DIRECT DIGITAL MANUFACTURING LABORATORY GEORGIA INSTITUTE OF TECHNOLOGY SLE Model Schematic Prior work: Single Bead ellipsoidal Profile Our work: SLE Model schematic showing powder placed on the top of a substrate and the laser beam rastering normal to the plane and stepping to the right. • Melt pool modeled as an ellipsoid. • Analytically normal component of solidliquid interface at different location can be calculated based on position vector. • Not suitable for the evaluation of the geometry parameters related to the melt pool in a raster scan pattern. Line source motion Instantaneous Laser position Liu, W. and J.N. DuPont, Acta Materialia, 2004. 52(16): p. 4833-4847. Our work: SLE simulated line heat source melt pool profile GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Overall SLE Modeling Methodology Immediate Consolidation Approach (CFX) Slow Consolidation Approach (FVM) Thermal Conduction o/p Boundary Convection Property= f(T) [*] Microstructure prediction o/p Saunder’s Database Melting and Resolidification i/p Sintering Model (Level Set) Powder bed Radiation (Ray Tracing) i/p Scan parameters Radiation Scan parameters Conjugate Heat Transfer o/p o/p Stress Analysis Substrate Temperature Distribution Fluid Convection Marangoni effect * Property values for CMSX-4 taken from - Matsushita et. al., Journal of Chemical & Engineering Data, 2009. 54 (9): p 2584-2592 GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Results - Melt Depth Prediction Representative operational parameters used Sample Powder Thickness (mm) Power (W) Scan Speed (mm/s) Number of Repeat Scan Sample 1 Sample 2 Sample 3 1 1 1.5 480 510 584 200 200 250 100 50 50 Sample 1 Sample 2 Sample 3 Animation of the transient temperature distribution and evaluation of the solid-liquid interface, and melt depth in SLE (click on the play button at bottom of picture) Plot of simulated melt depth (shown in red line) superimposed on the actual micrograph GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Microstructure Prediction Oriented-to-Misoriented Transition (OMT) Prediction Oriented-to-Misoriented transition occurs when the SX columnar orientation flips from 001 to 100 at some height above the substrate-deposit interface plane. Contour of orientation vector - Blue contour level indicates desired (001) orientation while red contour level indicates (100) orientation or possible OMT. The intermediate plane (dark blue) indicates the current laser position. DIRECT DIGITAL MANUFACTURING LABORATORY GEORGIA INSTITUTE OF TECHNOLOGY Results – OMT Prediction Orientation vector plot showing OMT for two different operating parameters Observation: 1. The orientation vector for (001) is assigned a value=1. The same is assigned a value of 10 for (010) and a value of 100 for (100). A value of zero indicates conflicting columnar orientation possibility. 2. Green contour level indicates desired (001) orientation while Orange contour level indicates (100) orientation or possible OMT. DIRECT DIGITAL MANUFACTURING LABORATORY GEORGIA INSTITUTE OF TECHNOLOGY Microstructure Prediction Columnar-to-Equiaxed Transition (CET) Prediction 1. 2. 3. 4. 5. 6. The Temperature gradient at the solid-liquid interface (G) is evaluated from CFD model. The solid-liquid interface orientation(θi) is evaluated from θi =cos-1(Gi/G) (for i=x, y, z). The growth rate of the solid-liquid interface is given by V= S cos θ (S= scan speed or Line source speed). the temperature gradient parallel to the dendrite growth direction, Ghkl, is calculated using the equation Ghkl =G/cos ; The dendrite growth velocity, Vhkl, is calculated using the equation V hkl = Scosθ/ cos . Rappaz Modification to predict CET: n= material constant(= 3.4 for CMSX-4), = equiaxed fraction( Critical value = 0.066%), N0= Nucleation density, ∆Ttip= Tip undercooling, ∆Tn= Nucleation undercooling 7. A lower value on the left hand side increases φ for a given nucleation density. Hence, a lower value of this ratio indicates likeliest location of columnar to equiaxed transition. DIRECT DIGITAL MANUFACTURING LABORATORY Contour of CET probability criterion The higher the value of Gn/V, the higher the probability of obtaining SX microstructure, while lower values indicate the probable locations of the CET. The intermediate plane (dark blue) indicates the current laser position. GEORGIA INSTITUTE OF TECHNOLOGY Results – CET Prediction (a1) (a2) (b1) (b2) CET criterion plot for same sample at (a1) heat source position 1= 7.9 mm, (b1) heat source position 2= 10.1 mm, (a2) and (b2) same with Marangoni effect Observation: 1. Time tracking is important!! 2. The CET criterion value varies with laser scan position. Marangoni convection reduces this value!! 3. From the contour plot, position 1 is likely to show a higher value of columnar height. DIRECT DIGITAL MANUFACTURING LABORATORY GEORGIA INSTITUTE OF TECHNOLOGY CET – Experimental Observations Experimentally observed CET at heat source position 1= 7.9 mm and (b) heat source position 2= 10.1 mm GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY CET – Experimental Observations Experimentally observed CET at heat source position 1= 7.9 mm and (b) heat source position 2= 10.1 mm Observation: 1. Position 1 shows CET at higher z-value. 2. Good agreement with the CET criterion value deduced from thermal and microstructure modeling. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Flow Field (a) (b) (a) Velocity vector observed in the liquid region and (b) Temperature map of the velocity vector in the melt pool. Observation: 1. Flow field consists of two rotational vortices, with zero velocity at center and scan source separating them from each other. 2. Loose powder at lower temperature is drawn from the front at some distance beneath the top surface. 3. The higher temperature melt pool expands at the top surface. 4. Natural convection observed at lower velocity with high temperature current going upward and then following the scan source. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Digital Micrograph Image Analysis-Based Automated Microstructural Feature Tracking Single Crystal Deposit Height Total Deposit Height Meltback Depth Sample Deformation Main microstructure features to be tracked in every sample for CMSX-4 (top and left) and Rene-80 (right). GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Digital Micrograph Image Analysis-Based Automated Microstructural Feature Tracking Original Substrate Location Bond Line Tracking • Bond line tracking relative to the original substrate location (top) allows for the meltback depth across the sample to be found. • Detailed dendrite tracking (left) allow for the [001] SX width, angle and termination point to be found across the sample length. 34 GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Digital Micrograph Image Analysis-Based Automated Microstructural Feature Tracking Bond Line Tracking • Bond line tracking in Rene-80 samples (top) allows for the meltback depth across the sample to be found. • Pore tracking (left) allow for the location, degree of circularity and size of each pore to be saved. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Digital Micrograph Image Analysis-Based Automated Microstructural Feature Tracking SX Termination Line • The SX termination line shows the point where the [001] single crystal microstructure ends in the sample. • Single crystal buildup is necessary for CMSX-4 samples. • Rene-80 and Marm 247 are concerned with the total deposit height. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Thermal Imaging-based Real-time Feedback Control Model-reference Adaptive Control Scheme (MRACS) • Nearly all thermophysical and optical properties vary with temperature, density and phase. • Thermal imaging-based absolute temperature itself is inaccurate due to unknown emissivity. • Simple PID control alone cannot account for variations in these properties during processing. • Using the MRACS scheme, the laser power and/or the scan speed will be adjusted in real-time to regulate the melt pool temperature. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Thermal Imaging-based Melt Pool Tracking • Use thermal camera to determine average melt pool temperature and width for use in feedback control • Thermal camera frame rate is 50-60fps and ideally would not be reduced significantly by tracking algorithm • Implemented algorithm based on Canny edge detection takes 1-2ms and reduces frame rate to 40-45fps • Algorithm is not yet fully optimized and running on old hardware, leaving room for improvement GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Thermal Imaging-based Melt Pool Tracking • Use thermal camera to determine average melt pool temperature and width. • Thermal camera frame rate is 50-60fps and ideally would not be reduced significantly by tracking algorithm. 1.) Acquire thermal image 2.) Convert to gray scale 3.) Apply Gaussian blur 4.) Binary threshold 5.) Canny edge detection 6.) Find bounding box GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY CONCLUSIONS • Fully dense, defect-free, porosity free deposits have been produced in wellknown hot section superalloys MARM-247, Rene 80, CMSX-4, . • Epitaxial solidification and microstructural continuity has been demonstrated on investment cast substrates of same alloy chemistry. • A comprehensive multiphysics modeling framework for the processing of alloy powders through SLE has been developed. • The predictive capability of the modeling framework has been validated for melting, solidification and microstructural evolution in single-crystal alloy CMSX-4 processed through SLE. • A quantitative metallography-based computational tool has been developed for microstructural characterization and DoE optimization. • Thermal-imaging based real-time adaptive control scheme should provide a tight control of the melt pool temperature, temperature gradients and cooling rates. • Coupled with detailed and approximate models, the real-time control scheme will enable layer-by-layer control of melting and microstructure uniformity, as well as the ability to produce functionally graded parts. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY SLE FUTURE APPLICATIONS AND IMPACT • Monolithic metal components of extreme geometric complexity and high efficiency that cannot be conventionally manufactured. – Turbine airfoils with non-castable complex cooling passages. • Monolithic metal components with dual or functionally graded microstructure and properties. – Next-generation functionally graded turbine airfoils with spatially tailored single-crystal, directionally solidified, and equiaxed microstructure with locally optimized properties. • Metal components with functionally graded alloy composition and properties. – Functionally graded turbine disk • Metal components with functionally graded distribution of ceramic nanoparticles – Oxide dispersion strengthened alloys GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY DoD Relevance • Repair and restoration to flightworthy condition of high value legacy components made of non-weldable and non-joinable alloys using same or similar alloy powders. • Directly digital manufacturing or remanufacturing of improved versions of various “in service” or legacy components from digital design/inspection data and feedstock powders. • Reduction in acquisition/repair costs and savings in lead time through the elimination of tooling, and reductions in processing steps, material waste, and energy consumption. • Future enabling technology for manufacturing next-generation, higher performance components of weapons systems exploiting advanced materials and microstructural configurations. GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY Thank you • Questions? 43 GEORGIA INSTITUTE OF TECHNOLOGY DIRECT DIGITAL MANUFACTURING LABORATORY
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