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.

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 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

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.

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.

Optimization of Magnesium Alloy Casting Process: An Integrated Computational Materials Engineering (ICME) Approach

2021 ◽  
Vol 1035 ◽  
pp. 808-812
Author(s):  
Xing Yang Chang ◽  
Qi Shen ◽  
Wen Xue Fan ◽  
Hai Hao
Keyword(s):  
Magnesium Alloy ◽  
Process Optimization ◽  
Casting Process ◽  
Mold Temperature ◽  
Pressure Casting ◽  
Alloy Casting ◽  
Materials Engineering ◽  
Integrated Computational Materials Engineering ◽  
Computational Materials ◽  
Low Efficiency

Traditional casting process optimization usually adopts empirical trial and error method. Process parameters were modified repeatedly within a certain range until a satisfactory solution is obtained, which is costly and inefficient. Therefore, based on integrated computational materials engineering, Magnesium Alloy Simulation Integrated Platform (MASIP) was constructed. MASIP completed the automatic operation of the entire simulation process from the CAD model data input to the process-microstructure-performance. It realized the rapid optimization simulation prediction of process-microstructure-performance, and solved the problems of long cycle and low efficiency of traditional process optimization. This paper studied the low-pressure casting optimization process of magnesium alloy thin-walled cylindrical parts based on MASIP. The calculation took casting temperature, mold temperature and holding pressure as the optimized variables, and the yield strength of the casting as the target variable. The experimental results showed that MASIP can fairly complete the structure simulation and performance prediction of castings, greatly reduce the time cost of the calculation process, and improve the efficiency of process optimization.

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