ICME: Status & Perspectives from Materials Science and Engineering Surya R. Kalidindi Georgia Institute of Technology
New Strategic Initiatives: ICME, MGI Reduce expensive late stage iterations Materials Genome Initiative
Systems-Based Concurrent Product and Materials Design/Development Olson, 1997 Goals/means (inductive) Processing Structure Limitations in Maturity, Interoperability, and Invertibility Properties Performance Cause and effect (deductive) Part Assembly System Quantum Atomistic Mesoscale Continuum Material Selection Courtesy: McDowell McDowell, Panchal, Choi, Seepersad, Allen, Mistree, Integrated Design of Multiscale, Multifunctional Materials and Products, 2009
Curate Materials Knowledge in the Structure Space Structural Materials Properties (Materials Selection) - OLD processing properties & responses Microstructure (Genome) - NEW Panchal, Kalidindi, McDowell, Journal of Computer-Aided Design, 2013 Courtesy: McDowell
Better Internal State Variables to Represent Material Response Materials-Manufacturing/Product Design Integration Grossly Simplified, Intuition-Based, Process-Structure-Property Linkages Materials Science and Engineering Multiple Heirarchical Structures Highly Complex Physics at Multiple Structure Scales Data Sciences & Cyberinfrastructure High Throughput Observations Objective Reduced-Order Representations Data-Driven Metamodels Product Design/ Manufacturing Simulations Complex Geometry of Parts Complex Boundary and Loading Conditions
Exploration of Process-Structure-Property Linkages Process Space Microstructure Space Properties Space Unimaginably large spaces inversion of the information flow is highly challenging using traditional approaches Highly effort intensive and costly Lack of standards (for data, tools, workflows, etc.)
Questions How can we bring together the advances in experiments, models, and data sciences to effect a beneficial integration of materials design with manufacturing and product design? How can we dramatically reduce the time and cost involved in the design and manufacture of new materials and products with targeted performance requirements?
Challenges: Experiments Capabilities exist for measuring structure at different length scales; Critically need protocols for measuring responses at different scales Most accessible measurements of structure are 2- D surface scans; Critically need protocols for statistically meaningful 3-D and 4-D datasets Need broadly adopted protocols for quantification of the hierarchical material structure Need protocols to measure directly the parameters in the multiscale material models Need high throughput (multimodal) measurement protocols to be cost and time effective
Challenges: Multiscale Materials Models Need, practically viable, multiscale materials modeling strategies Need standards for verification and validation of multiscale materials models Need standardization of inputs and outputs to enhance interoperability of models at different length/structure scales Need novel strategies for invertibility of information flow
Challenges: Data Sciences Need protocols for reduced order, sufficiently accurate, formulation of process-structure-property linkages needed for decision support in materials design, development, and deployment efforts Need suitably designed and deployed platforms for curating materials knowledge systems and integrating them with product design and manufacturing simulation tools
Examples of Ongoing Activities Addressing the Needs Identified Earlier
Measurements at the Right Scale Estimating properties of constituents and interfaces Nano-indentation combined with complementary local structure information Utilize computational tools for estimating the local properties of interest Study length scale effects by changing indenter size Facilitating rapid screening of alloys and composites 1 1 mm 2 3 6 9 11 4 5 8 10 7 C 11 (GPa) C 12 (GPa) C 44 (GPa) Literature* 168.4 121.4 75.4 Theoretical 162.5 117.9 72.38
High Throughput Multimodal Measurements for Enhancing Multiscale Model Maturity
3-D Measurements Using Stereology High throughput measurements of 3-D spatial correlations of orientation and stored energy in deformed samples
High Throughput Prototyping of Microstructures High throughput prototyping of microstructures through controlled thermal and/or mechanical gradients Combine strategies involving double- or single- cone tests with Jominy bars with numerical models (e.g., FEM) and indentation techniques
Microstructure Quantification The extracted dataset shows a microstructure, not the microstructure Need a framework for defining a hierarchical set of statistical measures of the microstructure at any selected length scale: n-point spatial correlations
Materials Knowledge Systems
The Materials Innovation Network is a collaborative environment built to fuel the Materials Genome Initiative by managing users and their digital data for microstructure driven materials development and improvement. COLLABORATION NETWORK Manages users, projects, and expert communities engaged in materials science related efforts. A melting pot for materials scientists, big data, and integration. CODE REPOSITORY A platform with an embedded versioning system to develop codes and deployable tools for the MATIN community at large. This platform will enable good coding practices and rapid delivery of academic utilities to market quickly. DATABASE The database is the unifying feature of MATIN. This graph database is specifically designed to store nearly all types of materials datasets, maintain data provenance, semantically query metadata, learn design patterns (or workflows), support the big data generation, and establish a federated database with access control for academia, industry, and national labs.
Questions??
Questions How can we move beyond artificial neural networks and extract the well-hidden constitutive equations for properties such as strength and fracture toughness? How can we utilize domain knowledge along with data sciences for better outcome? What are the most effective and computationally efficient methods for creating reduced order models for capturing process-structure-property linkages (i.e. core materials knowledge systems or databases)? Digital Data: How can we automatically curate data? How can we best capture data and metadata from scientific instrumentation? How do we package materials knowledge for effective integration with currently used commercial manufacturing process simulation tools?