Towards Computational Synthesis of Microstructural Crystalline Morphologies for Additive Manufacturing Applications

2017 ◽  
Author(s):  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
John C. Steuben ◽  
Andrew J. Birnbaum ◽  
Yao Fu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Phase Field Model ◽  
Diffuse Interface ◽  
Material System ◽  
Computationally Efficient ◽  
Metal Solidification ◽  
Integrated Computational Materials Engineering ◽  
Computational Materials ◽  
Manufacturing Applications ◽  
Manufacturing Technologies

Powder-based additive manufacturing technologies introduce severe variations in microstructure in terms of grain size and aspect ratio that, coupled with porosity, can result in dramatic effects on the functional (mechanical, thermal, fatigue, fracture etc.) performance of as-produced parts. In the context of Integrated Computational Materials Engineering (ICME), it is essential develop a computationally efficient approach for generating synthetic microstructural morphologies that reflect these process-induced features. In the present paper, we employ two methodologies for computing the evolution of metal solidification at the microstructural level as a function of process parameters associated with additive manufacturing. The first method is the Continuum Diffuse Interface Model (CDM) applied to an arbitrary material system, and the second, the Multi-Phase Field Model (MPFM) applied to pure nickel (Ni). We present examples of microstructures generated by these methods within the context of additive manufacturing.

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

JOM ◽  
2020 ◽  
Vol 72 (3) ◽  
pp. 1092-1104 ◽  
Author(s):  
S. Amir H. Motaman ◽  
Fabian Kies ◽  
Patrick Köhnen ◽  
Maike Létang ◽  
Mingxuan Lin ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Optimal Design ◽  
Metal Additive Manufacturing ◽  
Materials Engineering ◽  
Integrated Computational Materials Engineering ◽  
Computational Materials ◽  
Metal Additive

Optimize Additive Manufacturing Post-Build Heat Treatment and Hot Iso-Static Pressing Process Using an Integrated Computational Materials Engineering Framework

2018 ◽  
Author(s):  
Anahita Imanian ◽  
Kelvin Leung ◽  
Nagaraja Iyyer ◽  
Peipei Li ◽  
Derek H. Warner
Keyword(s):  
Heat Treatment ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Fatigue Performance ◽  
Optimization Approach ◽  
Materials Engineering ◽  
Integrated Computational Materials Engineering ◽  
Computational Materials ◽  
Static Pressing ◽  
Hip Process

Additive manufacturing (AM) technology is becoming more popular for the fabrication of 3D metal products as it offers rapid prototyping and large design freedom. However, part quality and fatigue performance of components fabricated by current AM technology are not comparable to that produced by traditional methods. Post-build processing techniques, such as heat treatment (HT) and Hot Iso-static Pressing (HIP), have been developed to improve microstructure and remove internal flaws that are detrimental to fatigue resistance. In order to simulate the HT and HIP process and optimize the post-build process, an integrated computational materials engineering (ICME) approach is utilized to link the process parameters with material’s structures, properties, and fatigue performance. The purpose of this study is two-fold. First, we simulate the HT/HIP process including the physics of heat transfer, and porosity evolution. Second, a state-of-the-art hybrid optimization approach, combining response surface method and genetic algorithm is utilized to optimize the post-build process parameters in order to minimize porosities.

scholarly journals Additive Manufacturing Applications on Flexible Actuators for Active Orthoses and Medical Devices

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Michele Gabrio Antonelli ◽  
Pierluigi Beomonte Zobel ◽  
Francesco Durante ◽  
Terenziano Raparelli
Keyword(s):  
Additive Manufacturing ◽  
Acrylonitrile Butadiene Styrene ◽  
Variable Stiffness ◽  
Fused Deposition Modelling ◽  
Research Projects ◽  
Element Analysis ◽  
Pneumatic Actuators ◽  
Fused Deposition ◽  
Manufacturing Applications ◽  
Manufacturing Technologies

This paper describes the results of research projects developed at the University of L’Aquila by the research group of the authors in the field of biomedical engineering, which have seen an important use of additive manufacturing technologies in the prototyping step and, in some cases, also for the realization of preindustrialization prototypes. For these projects, commercial 3D printers and technologies such as fused deposition modelling (FDM) were used; the most commonly used polymers in these technologies are acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). The research projects concern the development of innovative actuators, such as pneumatic muscles and soft pneumatic actuators (SPAs), the development of active orthoses, such as a lower limb orthosis and, finally, the development of a variable-stiffness grasper to be used in natural orifice transluminal endoscopic surgery (NOTES). The main aspects of these research projects are described in the paper, highlighting the technologies used such as the finite element analysis and additive manufacturing.

scholarly journals CMAS Effects on Ship Gas-Turbine Components/Materials

2018 ◽  
Author(s):  
D. A. Shifler ◽  
S. R. Choi
Keyword(s):  
Gas Turbine ◽  
Hot Corrosion ◽  
Past Research ◽  
Chemical Compositions ◽  
Material System ◽  
Integrated Computational Materials Engineering ◽  
Calcium Magnesium ◽  
Alumino Silicate ◽  
Computational Materials ◽  
Engine Components

Recent inspection of shipboard gas-turbine components under the platform has indicated the apparent presence of CMAS (calcium, magnesium, alumino-silicate) and its related attack. This type of attack has often been observed in aero gas turbine engines when sand and similar siliceous matter is ingested into the engine and the sand debris melts due to high engine operating temperature greater than 1150°C. Initial chemical analysis shows that the CMAS-affected areas of ship engine components versus aero engine components are similar. However, this phenomenon commonly observed in advanced aeroengines are not supposed to occur in the ship engine components since their probable temperature is known to be much lower than 1150°C (i.e., melting temperature of CMAS). As a consequence, some important questions arise as to: What caused this “CMAS” attack in ship engine components? Was this initiated by hot corrosion, which created a molten salt pool at a sufficient temperature to trigger CMAS attack? Did sodium chloride mixed with dust and debris lower the temperature at which molten CMAS would initiate? Past research provides a basic understanding of hot corrosion, but may ignore other reactants and other species inherently associated with ‘natural CMAS’ and mechanisms contributing to hot corrosion or CMAS attack. Further examination of ship and aero components will discern the local structure chemical profile of the component coatings, the chemical compositions of the alloy substrates, and the interface between the coating and the molten “CMAS” by several methods. Integrated computational materials engineering (ICME) and validating experiments will assist in developing degradation mechanisms. The environment complexity is also to be taken into account to determine whether salt-induced CMAS attack or CaO-induced hot corrosion may be dominant. The mechanisms need to be further studied and defined. The current work will address a series of systematic approaches to the aforementioned CMAS issues and will also present some recent results on CMAS-related effects on components and an elected alloy material system.

scholarly journals Heterogeneous objects representation for Additive Manufacturing: a review

2019 ◽  
pp. 14-23
Author(s):  
Luca Grigolato ◽  
Stefano Rosso ◽  
Roberto Meneghello ◽  
Gianmaria Concheri ◽  
Gianpaolo Savio
Keyword(s):  
Additive Manufacturing ◽  
Geometric Modeling ◽  
Object Representation ◽  
Functionally Graded ◽  
Geometric Modelling ◽  
Heterogeneous Objects ◽  
Manufacturing Applications ◽  
Manufacturing Technologies ◽  
Application Fields ◽  
Key Aspects

Recent advances in additive manufacturing technologies demand for extremely customized, complex shape and multi-fold functional products. Heterogeneous objects, such as functionally graded materials, represent an attractive solution for researchers and industries in many application fields. Combining geometric modelling and material assignment in a definitive and accessible CAD tool is still a challenge. In this review the key aspects of heterogenous object representation related to additive manufacturing processes are reported. After the presentation of the various methodologies for geometric modelling found in the literature, additive manufacturing applications for heterogeneous objects are summarized. Keywords: Geometric modeling; Computational geometry; Additive manufacturing; CAD; FGM; Heterogeneous objects

scholarly journals Understanding Hot Cracking of Steels during Rapid Solidification: An ICME Approach

2021 ◽  
Vol 3 (1) ◽  
pp. 30
Author(s):  
Fuyao Yan ◽  
Jiayi Yan ◽  
David Linder
Keyword(s):  
Additive Manufacturing ◽  
Rapid Solidification ◽  
Solid Phase ◽  
Hot Cracking ◽  
Materials Engineering ◽  
Integrated Computational Materials Engineering ◽  
Δ Ferrite ◽  
Quantitative Understanding ◽  
Computational Materials ◽  
Kinetics Of

Cracking is a major problem for several types of steels during additive manufacturing. Non-equilibrium kinetics of rapid solidification and solid–solid phase transformations are critical in determining the cracking susceptibility. Previous studies correlate the hot cracking susceptibility to the solidification sequence, and therefore composition, empirically. In this study, an Integrated Computational Materials Engineering (ICME) approach is used to provide a more mechanistic and quantitative understanding of the hot cracking susceptibility of a number of steels in relation to the peritectic reaction and evolution of δ-ferrite during solidification. The application of ICME and hot cracking susceptibility predictions to alloy design for additive manufacturing is discussed.

Multiphysics Integrated Computational Materials Engineering Linking Additive Manufacturing Process Parameters with Part Performance

2021 ◽  
pp. 293-338
Author(s):  
John Michopoulos ◽  
Athanasios Iliopoulos ◽  
John Steuben ◽  
Andrew Birnbaum ◽  
Nicole Apetre ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Functional Performance ◽  
Current Status ◽  
Naval Research ◽  
Grand Challenge ◽  
Main Research ◽  
Materials Engineering ◽  
Integrated Computational Materials Engineering ◽  
Computational Materials

The central goal of this chapter is to present an outline of the plan and current status of an effort to connect Additive Manufacturing (AM) process parameters with parameters describing the functional performance of produced parts. The term “functional performance” here represents primarily mechanical or thermal or electrochemical performance. The described effort represents an overview of the main research activities within a new multi-year grand-challenge project initiated at the US Naval Research Laboratory (US-NRL) in late 2016, in collaboration with groups from various academic institutions.

scholarly journals Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Xin Wang ◽  
Wei Xiong
Keyword(s):  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
High Strength ◽  
Nominal Composition ◽  
Structure Property ◽  
Target Composition ◽  
Structure Property Relationships ◽  
Integrated Computational Materials Engineering ◽  
Composition Optimization ◽  
Computational Materials

AbstractDuring powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process–structure–property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as to the initial composition, and optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.

An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built with Additive Manufacturing

2021 ◽  
pp. 1-26
Author(s):  
Behrooz Jalalahmadi ◽  
Jingfu Liu ◽  
Ziye Liu ◽  
Nick Weinzapfel ◽  
Andrew Vechart
Keyword(s):  
Additive Manufacturing ◽  
Fatigue Cracks ◽  
Materials Engineering ◽  
Microstructure Modeling ◽  
Integrated Computational Materials Engineering ◽  
Property Data ◽  
Multiple Material ◽  
Computational Materials ◽  
Experimental Validations ◽  
Metallic Parts

Abstract Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for Additive Manufacturing (DC-AM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DC-AM provides an efficient capability to more fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.

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