Materials Design Approaches and Experiences V: Alloy Design Strategy and Tools
Sponsored by: TMS Structural Materials Division, TMS: High Temperature Alloys Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Akane Suzuki, GE Aerospace Research; Ji-Cheng Zhao, University of Maryland; Michael Fahrmann, Haynes International; Qiang Feng, University of Science and Technology Beijing; Michael Titus, Purdue University
Monday 8:00 AM
February 24, 2020
Room: 33A
Location: San Diego Convention Ctr
Session Chair: Ji-Cheng Zhao, University of Maryland; Akane Suzuki, GE Research
8:00 AM Introductory Comments
8:05 AM Invited
Material and Process 4.0: Model-Based Material and Process Development: David Furrer1; Vasisht Venkatesh1; Jean Philippe Thomas1; 1Pratt & Whitney
Industry 4.0 and other terms are being used to describe the rapid change of engineering and industry toward a digital environment. Big data and data analytics are moving from business and finance industries to engineering design and manufacturing. These tools are opening up significant possibilities, but the largest opportunities reside in coupling physics-based models and data analytics. Material and Process 4.0 may be defined by the true integration of engineering, manufacturing and quality disciplines through modeling and simulation with uncertainty and probabilistics. This new era is giving rise of advanced modeling methods, sensors, data analytics, uncertainty quantification and linked computational methods. These new capabilities are resulting in more complete model-based product definitions enabling further utilization of material capabilities, smart testing and rapid part and process qualification. Though many aspects of this new infrastructure are being applied, many potential applications continue to emerge as these tools and methods become increasingly seamless.
8:35 AM Invited
Materials, Manufacturing, and Design: Perspectives on New and Nascent Techniques: Dennis Dimiduk1; Stephen Niezgoda2; Michael Groeber2; 1BlueQuartz Software LLC; 2The Ohio State University
The foundations of materials, manufacturing processes, and structures design are systematically shifting to ICME-based simulation workflows, married to tailored and specialized experiments that directly support the workflows. This evolution is driving a digital transformation throughout materials, processes, and structures engineering. In this talk selected examples are used to illustrate successes and limitations of ICME workflows. Also, we offer our perspectives on open frameworks, integration of digital environments, and aspects of machine learning and artificial intelligence tools, that show how exciting new frontiers are radically changing materials, manufacturing, and design for the future.
9:05 AM Invited
Accelerated Materials Design Strategy for Additive Manufacturing at Access e.V.: Ulrike Hecht1; Michael Mathes1; Daniel Röhrens1; 1Access e.V.
Based on experience with materials design for casting applications Access e.V. invested considerable effort into establishing a smart and fast methodology for the design of novel alloys and materials which can explicitely benefit from laser-based additive manufacturing. The key concept was to enable powder production, SLM-specimen production, microstructure analysis and micromechanical characterisation along a chain that involves a minimum amount of material. We shall present the lab-scale devices and main results from an ongoing alloy development featuring a high-entropy eutectic alloy. We shall discuss the requirements and challenges that emerge when thinking to set up a modelling chain to substantiate the experimental observations.
9:35 AM Break
9:55 AM Invited
Alloy Design Through Sequential Learning: James Saal1; 1Citrine Informatics
The state-of-the-art of alloy design relies on physical models and databases to map out the processing-structure-property relationships responsible for materials performance. Successful application of this approach to alloys has been demonstrated in steels, superalloys, and other structural alloys with an extensive history of development. However, alloy classes without such history lack accurate models or populated datasets to enable design. In such applications, materials informatics can accelerate the design workflow. Machine learning models can be trained on existing, sparsely populated data to derive correlation between processing-structure-properties in the absence of mechanistic understanding. An iterative process called sequential learning uses these machine learning models to identify the best experiments to perform to reduce model uncertainty and/or identify the highest-performing materials. Citrine’s experience using sequential learning to design alloys will be discussed, including the use cases of high entropy alloys (HEAs) for structural applications and thermoelectric half-Heusler solid solutions.
10:25 AM Invited
Further Developments of CALPHAD Based Tools for Accelerating Alloy Design: Paul Mason1; Adam Hope1; Kaisheng Wu1; Qing Chen2; Hai-Lin Chen2; Jiayi Yan2; Ralf Rettig2; Johan Jeppsson2; Anders Engstrom2; 1Thermo-Calc Software Inc.; 2Thermo-Calc Software AB
For more than 35 years, CALPHAD based tools have been used in the development of new alloys and improving the understanding and processing of existing alloys. Such tools and databases though are being continually developed and the recognition that CALPHAD is foundational to an ICME/MGI framework has further increased the demands on both software and database development to meet today’s challenges. This presentation will focus on four areas of development: 1) Better integrated thermodynamic and kinetic models and databases with illustrations using Thermo-Calc, DICTRA and TC-PRISMA, 2) CALPHAD databases with broader ranges of composition that are suitable for materials discovery and also materials processing/joining type applications. 3) Extension of the CALPHAD approach to modeling thermo-physical and thermo-mechanical properties, such as thermal conductivity and Martensite growth kinetics. 4) Improved understanding of how different computational approaches such as Ab Initio, CALPHAD and Machine Learning can be used together to accelerate materials design.
10:55 AM Invited
High-throughput CALPHAD Calculation for Accelerated Materials Design: Fan Zhang1; Chuan Zhang1; Weisheng Cao1; Jun Zhu1; Duchao Lv1; Shuanglin Chen1; 1CompuTherm LLC
Although the CALPHAD approach has been recognized as a key building block of the ICME concept, its potential has not been fully released due to the low efficiency when exploring the huge composition and temperature space of multicomponent systems. Recently, a high-throughput calculation (HTC) tool has been developed and implemented in PANDAT software. This HTC tool enables automatic calculation of thousands of alloys in user selected compositional space. Alloy compositions that satisfy materials design criteria can then be identified through data mining of the simulated results. Experimental work load can be significantly reduced by focusing only on the compositions identified by HTC. This tool can therefore help users to accelerate materials discovery and development through design. In this presentation, examples will be presented to demonstrate the applications of this tool in the design of Al-, Ni-, and Ti-based alloys, as well as high-entropy alloys.