Computational Materials Engineering Research Articles

Development of Aerospace Materials Using Integrated Numerical and Physical Simulation

The introduction of new alloys and process improvements that promise increased material performance to the aerospace and defense industries is a long and costly venture due to ensuring flight safety by way of data analysis and field service. Changes to the supply chain require the use of a phased approach, typically technical readiness level (TRL), to reduce risk. The techniques in the TRL methodology include both physical simulation, such as demonstrators, and computational simulation within the Integrated Computational Materials Engineering (ICME) framework. The typical approach consists of designing a methodology using computational processing, conducting pilot-scale trials, and using a TRL approach for scaling the technology. A balanced combination of physical and numerical simulations aids in understanding the role of metalworking processes in microstructure and property development. This in turn ensures the development of new and improved products in an accelerated manner. This paper reviews simulation methods, both computational and physical, available in the metals industry and discusses examples of how the use has accelerated deployment of new products.

Three-dimensional visualization and quantification of microporosity in aluminum castings by X-ray micro-computed tomography

Porosity is a major issue in solidification processing of metallic materials. In this work, wedge die casting experiments were designed to investigate the effect of cooling rate on microporosity in an aluminum alloy A356. Microstructure information including dendrites and porosity were measured and observed by optical microscopy and X-ray micro-computed tomography (XMCT). The effects of cooling rate on secondary dendrite arm spacing (SDAS) and porosity were discussed. The relationship between SDAS and cooling rate was established and validated using a mathematical model. Three-dimensional (3-D) porosity information, including porosity percentage, pore volume, and pore number, was determined by XMCT. With the cooling rate decreasing from a lower to a higher position of the wedge die, the observed pore number decreases, the porosity percentage increases, and the equivalent pore radius increases. Sphericity of the pores was discussed as an empirical criterion to distinguish the types of porosity. For different cooling rates, the larger the equivalent pore radius is, the lower the sphericity of the pores. This research suggests that XMCT is a useful tool to provide critical 3-D porosity information for integrated computational materials engineering (ICME) design and process optimization of solidification products.

Multi-component numerical simulation and experimental study of dendritic growth during solidification processing

A multi-component model was developed to simulate microstructure evolution during solidification of a quinary alloy system. The thermodynamic and kinetic parameters including liquidus slope, partitioning coefficient, diffusion coefficient etc. as functions of temperature and solute concentration were obtained using CALculation of PHAse Diagrams (CALPHAD) modeling. The effect of solute interactions on diffusion and dendrite growth velocity was fully considered in this model. The model was validated by mesh size convergence, and compared with a well-accepted analytical model. The new model was shown to successfully display solidification dendrites and solute redistributions of a quinary Al-Si-Mg-Fe-Mn alloy. The effect of different solute elements on solute diffusion and dendrite growth was analyzed. Additionally, various calculation domains and cooling rates of industrial importance were tested in the simulations. Directional solidification experiments were used for model validation in secondary dendrite arm spacing measurement and simulation. The validated relationship between the secondary dendrite arm spacing and cooling rate in a complex quinary system confirmed the accuracy of this 3-D multi-component model, which provides a critical connection between solidification processing and mechanical property prediction in Integrated Computational Materials Engineering (ICME) of solidification products.

Prediction of location specific mechanical properties of aluminum casting using a new CA-FEA (cellular automaton-finite element analysis) approach

Predicting location specific properties of industrial castings is a critical part of the Integrated Computational Materials Engineering (ICME) framework for casting design and manufacturing. In this paper, a new design methodology is presented to calculate and predict location specific mechanical properties of an aluminum casting based on its location specific microstructure. Cellular Automaton (CA) and Finite Element Analysis (FEA) tools are coupled to model the mechanical response of a cast component from the casting process conditions to its final location specific solidification microstructure. This new CA-FEA methodology can bridge multiple length scales and predict location specific microstructure and mechanical properties of an aluminum wedge casting, which was experimentally validated using X-ray Micro Computed Tomography and mechanical testing.

Material Allowable Generation and AM Process Parameters Effect on Porosity

Additive manufacturing (AM) process methods such as powder bed fusion (LPBF) of metal powder layers can produce layered material systems with designed microstructures, which may exhibit scatter in mechanical properties (e.g., lower yield and lower failure strain), corrosion due to porosity and print anomalies. This study shows the development of AM process simulation to predict As-built material characteristic and their scatter comparing with experimental test data. ICME (Integrated Computational Materials Engineering) was used to simulate yield, ultimate, strain, and reduction of the area of sample AM. The method was extended to predict oxidation and damage of as-built parts. The samples were fabricated horizontally and vertically in multiple and scatter directions to find the effect on the mechanical properties such as ultimate tensile strength (UTS) and yield strength (YS). The probabilistic sensitivities show that in order for the next-generation technology to improve the strength of 3D printed materials, they must control the void volume fraction (trapped gas) and orientation of voids. The studied 3D print modality processes: (a) LPBF of AlSi10Mg, and (b) Electron Beam (EBM) of Ti-6Al-4V materials are shown to be over 99.99% reliable. The statistics of 3D printed Ti-6Al-4V have been observed for room and high temperature (RT/HT). The ICME Material Characterization and Qualification (MCQ) software material model prediction capabilities were used to predict (a) Material Allowable, a variation in Stress Strain Curves Characteristic Points and Residual Stress due to air particle (void/defect) shape and size and orientation. The probabilistic simulation computes Cumulative Distribution Function (CDF) and probabilistic sensitivities for YS, UTS, and %Elongation as well as A and B basis allowable of the As-Built 3D printed material and; and (b) Fracture Control Plan fracture toughness determination, and fatigue crack growth vs. stress intensity.

DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing

Abstract A data identifier (DID) is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering (ICME), inheritable integrated intelligent manufacturing (I3M), and the Industrial Internet of Things. With the guidance and quick acceleration of the development of advanced materials, as envisioned by official documents worldwide, more investigations are required to construct relative numerical standards for material informatics. This work proposes a universal DID format consisting of a set of build chains, which aligns with the classical form of identifier in both international and national standards, such as ISO/IEC 29168-1:2000, GB/T 27766–2011, GA/T 543.2–2011, GM/T 0006–2012, GJB 7365–2011, SL 325–2014, SL 607–2018, WS 363.2–2011, and QX/T 39–2005. Each build chain is made up of capital letters and numbers, with no symbols. Moreover, the total length of each build chain is not restricted, which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646. Based on these rules, the proposed DID is flexible and convenient for extending and sharing in and between various cloud-based platforms. Accordingly, classical two-dimensional (2D) codes, including the Hanxin Code, Lots Perception Matrix (LP) Code, Quick Response (QR) code, Grid Matrix (GM) code, and Data Matrix (DM) Code, can be constructed and precisely recognized and/or decoded by either smart phones or specific machines. By utilizing these 2D codes as the fingerprints of a set of data linked with its cloud-based platforms, progress and updates in the composition–processing–structure–property–performance workflow process can be tracked spontaneously, paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity, performance, and collaboration.

Light-Weighting Technologies for High-Performance Ductile Iron Sand Castings

There have been recent advances with high-strength high-ductility alloys and 3D (dimensional) sand printing additive manufacturing technologies. These advances make for original equipment manufacturer casting design engineers to come up with newer, innovative design solutions. Similarly, light-weighting technologies coupled with integrated computational materials engineering (ICME) tools and latest strain-life fatigue data optimize the process of casting configurations. This paper discusses thin-wall casting designs and optimization process with case studies, including the effective use of the ICME tool, casting process modelling, and toolingless 3D printed sand additive manufacturing processes. The approach of taking current legacy cast parts while utilizing thin-wall technologies is discussed within the case studies. Studies also cover casting conversion opportunities for competing materials and processes: steel fabrications, steel castings, forgings and aluminium castings for high-performance applications.

Optimal Design for Metal Additive Manufacturing: An Integrated Computational Materials Engineering (ICME) Approach

We present our latest results on linking the process–structure–properties–performance (PSPP) chain for metal additive manufacturing (AM), using a multi-scale and multi-physics integrated computational materials engineering (ICME) approach. The abundance of design parameters and the complex relationship between those and the performance of AM parts have so far impeded the widespread adoption of metal AM technologies for structurally critical load-bearing components. To unfold the full potential of metal AM, establishing a full quantitative PSPP linkage is essential. It will not only help in understanding the underlying physics but will also serve as a powerful and effective tool for optimal computational design. In this work, we illustrate an example of ICME-based PSPP linkage in metal AM, along with a hybrid physics-based data-driven strategy for its application in the optimal design of a component. Finally, we discuss our outlook for the improvement of each part in the computational linking of the PSPP chain.

Data Driven Tools and Methods for Microtexture Classification and Dwell Fatigue Life Prediction in Dual Phase Titanium Alloys

Microtexture has been linked to large reductions in cold dwell fatigue (CDF) life of specific dual phase titanium alloy aeroengine components. A recently completed Metals Affordability Initiative (MAI) funded program led by Pratt & Whitney (P&W) and includes ATI Forged Products, Boeing, GE Aviation, Rolls Royce (RR), Arconic, Titanium Metals Corporation (TIMET), PCC-Wyman Gordon (PCC-WG), Scientific Forming Technologies (SFTC), Materials Resources LLC (MRL) and The Ohio State University (OSU) has developed improved techniques for the characterization of microtexured regions (MTR) in titanium billet and forgings, and integrated computational materials engineering (ICME). These methods are aimed at developing and integrating process and property modeling tools for the prediction of microtexture and fatigue life in titanium components. These characterization and fatigue life prediction tools have near-term application off ramps that will enable use for process and product development and quality control. Key results for two widely used alloys, Ti-6242 and Ti-64, will be reviewed in this paper.

Titanium alloy design and casting process development using an Integrated Computational Materials Engineering (ICME) approach

The application of titanium components is generally limited by their high raw material and manufacturing costs. In this paper, a lower cost cast titanium alloy based on the Ti-Al-Fe system has been designed using an ICME approach. The new alloy Ti-6Al-5Fe-0.05B-0.05C (all wt.%) significantly reduces raw material cost and demonstrates improved castability compared with the baseline Ti-6Al-4V alloy. The fine primary and secondary α phase microstructure in the new alloy, due to Fe partitioning, provides exceptionally high strength (1023 MPa yield strength and 1136 MPa ultimate tensile strength) and reasonable ductility (3.7% elongation) for structural applications. On the manufacturing front, the high cost multi-step investment casting process currently used can now be replaced with a low-cost permanent mold casting process using steel molds and a novel ceramic coating. An experimental casting setup, including an induction skull melting (ISM) system, a gravity tilt-pour system and a ceramic-coated H13 steel mold, has been used to produce near-net-shape permanent metallic mold castings with the new titanium alloy developed. Using this setup, and aided by casting process simulation, a prototype automotive connecting rod was cast successfully. The ZrO2 ceramic coating applied to the H13 steel mold was proven effective in minimizing the metal-mold reactions.

The need for reliable data in computational thermodynamics

Computational methods have become indispensable tools for efficient development and processing of new materials and have led to the new discipline of integrated computational materials engineering (ICME). The CALPHAD (calculation of phase diagrams) method has been identified as one of the pillars of ICME. The CALPHAD method, originally developed to model thermodynamic properties and phase diagrams, uses extrapolation methods for the functions of binary and ternary systems that enable the calculation of the properties of higher order systems. The CALPHAD functions are built to a large extent on available experimental data for these binary and ternary systems. To ensure reliability of the results from CALPHAD calculations, it is necessary to critically evaluate the experimental data that are being used for developing the CALPHAD functions. This review presents a brief overview of the CALPHAD method and its models, summarizes the data that are needed and the criteria that need to be applied for the evaluation of these data.

Advanced Titanium Alloy Fatigue Modeling

A summar y of the final progress achieved in two Metals Affordability Initiative (MAI) programs (RR-12 and RR-13) that were funded by the US Air Force to develop the necessary integrated computational materials engineering (ICME) framework, knowledge, and supporting database to model and predict location-specific fatigue properties across the entire titanium supply chain is presented. Validation of this ICME framework which allows for the prediction of location specific low cycle fatigue (LCF) and high cycle fatigue (HCF) behavior on complex production components in electro-polished and shot peened surface conditions will be presented. In addition, validation of a new shot peening capability embedded with the commercial software package DEFORM™ will be presented.

Integrated Computational Materials Engineering of Gamma Titanium Aluminides for Aerospace Applications

Although the benefits of titanium aluminides for intermediate service temperature applications were well conceived and significant research and development activities were conducted in the past four decades, they remained as developmental materials due to barriers associated with melting, processing, scale-up, and cost. Demanding requirements of efficient aero-engines and extensive risk reduction demonstrations paved the path for commercial introduction of gamma titanium aluminides. The single most attractive current application is for low pressure turbine blades (LPTBs) in advanced aero-engines replacing conventionally cast nickel superalloys. This paper provides an overview of recent progress, producibility challenges, and opportunities. The successful journey of gamma (γ) TiAl LPTB development from laboratory demonstrations to production insertions in mass-produced commercial jet engines will be described. Collaboration and integrated product development were identified as the most critical needs for rapid maturation and implementation of γ-TiAl into aerospace applications. An integrated computational materials engineering modeling framework and toolsets developed under a collaborative US Air Force Metals Affordability Initiative project between industry, government, and academia will be illustrated. Model-based optimization of material and processing for achieving desired performance goals will be highlighted.

An integrated computational materials engineering-anchored closed-loop method for design of aluminum alloys for additive manufacturing

A closed-loop approach based on integrated computational material engineering was used to design, fabricate and characterize an Al–1.5Cu–0.8Sc–0.4Zr (wt%) alloy for laser powder bed fusion additive manufacturing (AM). Finalization of composition and prediction of solidification behavior and mechanical properties were done using calculation of phase diagrams (CALPHAD) and analytical tools. The microstructure of the printed alloy in as-built condition consisted of crack-free regions with fine-equiaxed grains which was consistent with CALPHAD results. Yield strength (YS) of ~349 ± 8 MPa was also in more than 90% agreement with predicted YS. The findings demonstrate an efficient methodology for application-based alloy design for AM.

Computational Efficient Modeling of Sintering in Multi-component Alloys for ICME Applications

The major challenge while using sintering models for simulation of densification in multi-component alloys is finding the correct transport parameters, which are affected by not only temperature but also chemical composition and phase dispersion. A novel approach for determining the effective self-diffusivity and hence modeling the densification of engineering alloys during sintering is proposed. The approach integrates computational thermodynamics and simulation of diffusion-controlled transformations in multi-component alloys together with a low-order model for solid-state sintering. Computational thermodynamics, using the CALPHAD method, is used to predict microstructural phase stability, which is then used by diffusion simulation models to evaluate the effective transport properties for the sintering model. The modeling approach is validated by comparing results for densification of precipitation-hardened and austenitic stainless-steel alloys during an iso-rate sintering schedule with data from the literature. It is shown that the model can capture experimental observations very well. The modeling approach can thus be used in the development of an efficient search methodology for particulate materials within the context of an integrated computational materials engineering (ICME) frameworks.

ICME Framework for Damage Assessment and Remaining Creep Life Prediction of In-Service Turbine Blades Manufactured with Ni-Based Superalloys

Accurate creep life prediction is necessary for the evaluation of the remaining creep lives of in-service turbine blades and for the design of new turbine blades in aircraft engines. In this study, an integrated computational material engineering methodology for predicting the remaining creep life of in-service turbine blades was developed by taking a microstructural criterion and creep strain criterion into consideration, and combining artificial neural networks with a modified θ projection model to assess the service temperature, stress, degradation time, and existing creep strain. To explore the application of the method and verify its accuracy, the microstructural degradations at different locations of two directionally solidified superalloy DZ125 turbine blades, which were in-service for 300 h and 980 h in different engines, were characterized and quantified. Using these results, the remaining creep life of the microstructures at different locations of the blade was predicted. Finally, these creep life prediction results were experimentally verified using miniature creep test specimens. The development of this new method provides a reference for the design and service evaluation of turbine blades made of directional solidified and single-crystal Ni-based superalloys.

Process-Structure-Properties-Performance Modeling for Selective Laser Melting

Selective laser melting (SLM) is a promising manufacturing technique where the part design, from performance and properties process control and alloying, can be accelerated with integrated computational materials engineering (ICME). This paper demonstrates a process-structure-properties-performance modeling framework for SLM. For powder-bed scale melt pool modeling, we present a diffuse-interface multiphase computational fluid dynamics model which couples Navier–Stokes, Cahn–Hilliard, and heat-transfer equations. A computationally efficient large-scale heat-transfer model is used to describe the temperature evolution in larger volumes. Phase field modeling is used to demonstrate how epitaxial growth of Ti-6-4 can be interrupted with inoculants to obtain an equiaxed polycrystalline structure. These structures are enriched with a synthetic lath martensite substructure, and their micromechanical response are investigated with a crystal plasticity model. The fatigue performance of these structures are analyzed, with spherical porelike defects and high-aspect-ratio cracklike defects incorporated, and a cycle-amplitude fatigue graph is produced to quantify the fatigue behavior of the structures. The simulated fatigue life presents trends consistent with the literature in terms of high cycle and low cycle fatigue, and the role of defects in dominating the respective performance of the produced SLM structures. The proposed ICME workflow emphasizes the possibilities arising from the vast design space exploitable with respect to manufacturing systems, powders, respective alloy chemistries, and microstructures. By digitalizing the whole workflow and enabling a thorough and detailed virtual evaluation of the causal relationships, the promise of product-targeted materials and solutions for metal additive manufacturing becomes closer to practical engineering application.

Integrated computational materials engineering simulation studies of nuclear alloys based on crystal plasticity modeling

For nuclear reactor, alloys with excellent corrosion resistance and mechanical properties are widely used in the nuclear fuel cladding, internal component, and steam generator. The processing technology affect the microstructure directly, and hence the mechanical and corrosive properties. It is an important step to study on the performance of nuclear alloys outside reactor to alloy evaluation and improvement. Hence it is significant to predict the “processing-microstructure-mechanical property” of nuclear alloys. Conventional methods just rely on experimental “try-and-error” cycle, which costs lots of money and takes long time. Integrated Computational Materials Engineering based on through process simulation try to combine the models of processing, microstructure evolution and property, can remarkably reduce the R&D period and expense. To promote the application of this methodology in the processing optimization and development of nuclear alloys, the present study employed microstructure-sensitive crystal plasticity models and decoupled finite element simulation technique, to study quantitatively the “processing-microstructure-mechanical property” interrelationship of Zr, Ti, and FeCrAl alloys. The results indicated that the present model was able to predict accurately the hot processing texture and the corresponding tensile/compressive plastic behavior of three typical nuclear alloys. The present simulation method based on crystal plasticity can provide computational tool and reference to processing optimization and mechanical property evaluation of nuclear alloys.

Studying Grain Boundary Strengthening by Dislocation-Based Strain Gradient Crystal Plasticity Coupled with a Multi-Phase-Field Model.

One ambitious objective of Integrated Computational Materials Engineering (ICME) is to shorten the materials development cycle by using computational materials simulation techniques at different length scales. In this regard, the most important aspects are the prediction of the microstructural evolution during material processing and the understanding of the contributions of microstructural features to the mechanical response of the materials. One possible solution to such a challenge is to apply the Phase Field (PF) method because it can predict the microstructural evolution under the influence of different internal or external stimuli, including deformation. To accomplish this, it is necessary to take into account plasticity or, specifically, non-homogeneous plastic deformation, which is particularly important for investigating the size effects in materials emerging at the micron length scale. In this work, we present quasi-2D simulations of plastic deformation in a face centred cubic system using a finite strain formulation. Our model consists of dislocation-based strain gradient crystal plasticity implemented into a PF code. We apply this model to study the influence of grain size on the mechanical behavior of polycrystals, which includes dislocation storage and annihilation. Furthermore, the initial state of the material before deformation is also considered. The results show that a dislocation-based strain gradient crystal plasticity model can capture the Hall-Petch effect in many aspects. The model reproduced the correct functional dependence of the flow stress of the polycrystal on grain size without assigning any special properties to the grain boundaries. However, the predicted Hall-Petch coefficients are significantly smaller than those found typically in experiments. In any case, we found a good qualitative agreement between our findings and experimental results.

Growth interactions between icosahedral quasicrystals

We investigate the solidification pathways of a population of icosahedral quasicrystals in a liquid through in situ synchrotron x-ray tomography. The wealth of three-dimensional space- and time-resolved data enables us to test the predictions of various models and theories of crystallization on a quasi-crystal-forming alloy. Remarkably, we find the general evolution equation---that accounts for the competing effects of growth, coalescence, and coarsening---fits well our experimental data on surface area concentration. Furthermore, we quantify the orientation selection, screening length, and coarsening rate of the dodecahedra, and compare these results to that of periodic crystals. The latter is a full order of magnitude smaller than that of elemental metal dendrites in the limit of zero volume fraction, a reflection of the low solid-liquid interfacial energy of the icosahedral phase. Our paper provides the critical input data for microstructural models used for integrated computational materials engineering of complex intermetallics, including quasicrystals.