Computational Materials Design. Tomi Suhonen, Anssi Laukkanen, Matti Lindroos, Tom Andersson, Tatu Pinomaa, et al. VTT Materials & Manufacturing

Transcription

1 Computational Materials Design Tomi Suhonen, Anssi Laukkanen, Matti Lindroos, Tom Andersson, Tatu Pinomaa, et al. VTT Materials & Manufacturing

2 Contents Click to edit Master title style Computational materials design, focusing especially on microstructures of metallic and composite materials subjected to mechanical loading (fatigue, fracture, wear ) Direct microstructural modifications and virtual trials of modern martensitic steels Solidification structures, properties and performance of additively manufactured metals High entropy alloy accelerator Machine learning in capturing microstructural design parameters 2

3 Process-Structure-Properties-Performance Concept in Application of ICME Click to edit Master title style 3

4 Microstructural modeling: model generation Click to edit Master title style Martensitic steel microstructure Prior austenite grains reconstructed Merger of advanced characterization and modeling means provides quite a realistic description of steel at the microstructural level Hierarchies, such as block boundaries (green) and packet boundaries (red) Computational microstructure 4

5 Microstructural modeling: model generation EBSD orientations Click to edit Master title style from characterization 3D microstructural model geometry from a stack of 2D images Material plot from finite element model Example of a martensitic steel microstructural model Utilizing EBSD data as basis for model generation, either statistical or directly imaging based model 5

6 Constitutive models: Micromorphic and higher order single crystal models for martensitic and alike microstructures Click to edit Master title style 100 um 10 um Motivation: Significant increase in flow stress with small microstructural features Capture the effect of length-scale E.g., smaller grain size Hall-Petch effect Geometrically necessary dislocations become more significant when grain size is small In martensitic steels: martensite hierarchy enables small grains/laths and retained austenite exist in the microstructure in small/large islands Implementation to crystal plasticity models Regularization of damage zones Stabilization of shear banding Tailoring of length scale dependent microstructures Cordero & Forest 6

7 Constitutive models: Gradient plasticity + TRIP Click to edit Master Voltitle fraction of style all Retained austenite grain (for testing purposes) grains (not local austenite grain only) 1000 um 100 um 10 um Generalized stress (Sx) 1000 um 10 um Equivalent stress (Mises) Transformed volume fraction FCC BCC 7

8 Click to edit Master title style Analysis workflows and use cases for wear and fatigue 8

9 Micromechanics of abrasive wear Modeling Workflow Click to edit Master title style 9

10 Single asperity scratch test models, martensitic steel grade Scratch testing of martensitic steel grade, contours of cumulative plastic slip Material section plots 18/06/

11 Microstructure design trial: Fully martensitic grade Click to edit Master title style Synthetic microstructural models: The core methodology for generating statistical and parametric microstructures, focus on martensite like features. Kitahara et al, st scale: inherent austenite grains (either isotropic or like in current example with max 1/10 aspect ratio) 2 nd scale: first submorphology, for martensite microstructures the packet microstructure 3 rd scale: further morphologies, at minimum for martensite the block structure 11

12 Microstructure design trial: Fully martensitic grade: example on prior austenite grain aspect ratio Click + ODF to edit Master title style Prior austenite grain aspect ratio = 1 Prior austenite grain aspect ratio = 2 Prior austenite grain aspect ratio = 5 Prior austenite grain aspect ratio = 10 12

13 Microstructure design trial: Fully martensitic grade: example on prior austenite grain aspect ratio Click + ODF to edit Master title style Prior austenite grain aspect ratio = 1 1 st principal stress Cumulative plastic slip Cumulative micromechanical model damage sum: Damage sum Prior austenite grain aspect ratio = 5 Prior austenite grain aspect ratio = 10 Scratch length 13

14 Microstructure informed abrasion damage and cumulative Click to edit wear Master title style Establish surface wear inducing loading load case 2, higher velocity load case 1, lower velocity Microstructure informed modeling of wear damage crystal plasticity analysis of slip and damage for differing wear events wear rate surface of a component, component mass loss and surface damage update analysis & iterate further when required (e.g. wear surface changes etc.) Coarse-grain with respect to temporal scale Coarse-grain with respect to spatial scale divide the wear process volume to regions & evaluate statistically 14

15 Modeling abrasive wear loading in 2- and 3- Click body to contacts edit Master title style Modeling abrasive wear loading arising from 2- and 3-body abrasion. Wear resistant steel plate and the collapse of a rock column and a velocity of approx. 50 m/s at a nominal angle of 50 degrees. 15

16 Modeling abrasive wear loading in 2- and 3- Click body to contacts edit Master title style Modeling abrasive wear loading arising from 2- and 3-body abrasion. Wear resistant steel plate moving laterally with a velocity of 10 m/s. 16

17 Experiments & Characterization Click to edit Master title style Application Characterization Experiments Simulations Macro-to-micro scale Stress Microscale Application scale model Twins 17

18 Click to edit Master title style Visualization Crystal plasticity approach Realistic type rock inputs Dynamic model Crusher stresses, deformation, and wear Crusher loads Crushing efficiency End product quality Crushing work & energy Material performance 18

19 Use case: design for metal additive manufacturing

20 Multiscale modeling for metal additive manufacturing Powder and alloy design Material property & performance design Modeling material structure properties and performance Discrete modeling of powder bed physics SLM process design and optimization Material structure to material properties causality Material performance Powder bed thermomechanics, laser matter interaction Thermomechanical modeling of selective laser melting PRODUCT PERFORMANCE AND COST Thermodynamics and phase fields Part geometry design Topology optimization Solidification microstructure, surface phenomena & reactive wetting Part specific process design, residual stress & distortion minization Optimized geometric design 18/06/

21 Modeling for metal additive manufacturing: Concept Adopting ICME principles Motivation: to properly design for metal AM, an approach incorporating aspects of material, process and product modeling and design is required. (1) (2) (3) (4) (5) Digital material, digital manufacturing and digital product design for metal additive manufacturing. Enable complex and coupled (e.g. two-way) optimization workflows. 18/06/

22 Model Generation & I/O with SLM Machine Model creation via a push of a button : 1) interface to read and process machine build files (and logs) and 2) create the thermomechanical process model directly for simulation with a solver of interest. Use e.g. with SLM or EOS systems, integration via log and build files. Geometry parsed from bracket,example layers 1 & 100 Parsing scan strategy from CLI Reproduce scan strategy /06/2018 Extensible since the basis classes describing the AM build process have been developed and established for typical SLM and EOS systems. 22

23 SLM transient thermomechanical process model, bracket case study Bracket geometry of this case study, approx. 2k layers in experimental build. Temperature isosurfaces, example from layer 1100, 20 by 20 cm powder bed in model. Linking between local thermal solution, process parameters, scan strategy and part features Laser power P = 100 W, beam velocity v = 1000 mm/s Laser power P = 150 W, beam velocity v = 1000 mm/s Laser power P = 250 W, beam velocity v = 1000 mm/s Laser power P = 375 W, beam velocity v = 1000 mm/s 18/06/

24 Selective laser melting of Ti-6Al-4V and PF modeling Click to edit Master title style Epitaxial growth of columnar grains Epitaxial microstructure 24

25 Structure-Property: Crystal plasticity, tensile testing Click to edit Master title style CUMULATIVE PLASTIC SLIP Columnar model 1, direction 1 Columnar model, lamellar substructure EQUIVALENT STRESS CONTOURS Equiaxed model, lamellar substructure Columnar model, lamellar substructure Equiaxed model Equiaxed model Equiaxed model, lamellar substructure Columnar model 1, direction 1 Columnar model 1, direction 2 25

26 Fatigue performance indicators (F-S, D-V etc.) Effect Click of loading conditions to edit and microstructural Master detail title to cycles style to initiation prediction FPIs in general: ~100 cycles in strain control, merged results from F-S and damage computation Best microstructure as good as the others for large amplitudes defects Coarser structure, effect approx Fine structure Introduction of a short fatigue crack size sharp defect influences cycles to initiation by a factor of approx Effect of inclusion with good adhesion approx

27 SLM transient thermomechanical process model, Click drill to edit piston Master case title study style Map smaller scale modeling results to larger components by considering local thermal solution during build: oil pressure distributor valve in a rock drill piston (topology optimized) Computation of representative thermal histories for material points Self-consistent crystal plasticity model for computing local stress-strain response Different process parameters yield a differing (homogenized) material response in terms of strength (phase distribution in the component), the effects of which are highlighted in a case study analysis. Component deformation response during the design performance critical axial impact, utilizing the process parameters yielding the most suited mechanical properties and higher surface strength (left) (P = 190 W, v = 1.2 m/s) and a set with less favourable response and lower surface strength (right) (P = 230 W, v = 0.9 m/s). The component in the right is with respect to fatigue amplitude in a low cycle fatigue region, the one on the left in the high cycle region. 27

28 Use case: material discovery and high entropy alloys

29 Process-Structure-Properties-Performance Concept in Application of ICME Click to edit Master title style Artificial intelligence and machine learning 29

30 MATERIAL DISCOVERY GUIDING PRINCIPLES Need to be able to discover and generate new data (~discovery by MD) 2. Need to generate lots of the data in question (~automatization required) 3. Data can be imperfect (~machine learning of trends and parameters) 4. Ultimately drive the process (~ AI ) 18/06/ MATERIAL DEVELOPMENT & INTEGRATION TO PRODUCT

31 CrMnFeCoNi base alloy, FCC single crystal, applied shear 18/06/

32 Findings from the atomistic recipe book Tweaking Ni content to activate different deformation mechanisms Enable deformation, promote toughness Limit deformation, promote strength and hardness Promote deformation over twinning and phase transformations Increased twinning of the FCC phase 18/06/

33 Data-based modeling: Machine learning for CP

34 Effects of sub-grain structures to micro-scale behavior of martensitic steel aggregates Simple example how microstructure modeling resolution and inclusion of martensite sub-hierarchies influences the analysis outcome: Different levelof discretization Versatile tools for model generation and modification required to be able to run design iterations: i) Computational geometry based (tesselations, packings etc. typical operations) ii) Image processing and microstructure (re)construction tools, operating largely on morphological imaging and characterization data Simplest means to exploit multi-level tesselations etc. for construction. 18/06/2018 mesh equivalent stress equivalent plastic 34 strain

35 Synthetic microstructures for machine learning models Click Texture to edit follows Master K-S variants title style Tensile test (~similarity in behavior) Microstructure affects the stressstrain behavior Prior austenite + martensite packets included Microstructure A Coarse structure Microstructure B Mixed structure Microstructure C Small prior austenite 35

36 Machine learning for the crystal plasticity results Click to edit Master title style Apply machine learning to recognize grain features that predict plastic slip, hardening Click to for edit evaluation Master of text fatigue styles performance Second indices level Obtain physical understanding Relationships Fourth between level microstructure and fatigue performance Find general rules to help design and act as surrogate models in design workflows Features for the model: Aims at full field prediction Crystal orientation distribution Schmid factors Geometric features (grain shape, size, etc ) Prediction accuracy on top-right: R 2 = 60% Cumulative plastic slip predictions compared for all data: Cumulative plastic slip predictions compared for 2D models: Crystal plasticity model Machine learning model 36

37 Thank You! Acknowledging collaborations with: Financially supported by: RFCS project IMMARS (funded by EC) Manufacturing, Micromechanics and Numerics project (funded by TEKES) Fundamentals and modeling materials programme (funded by TEKES)