Multiscale Modeling of Additively Manufactured Metals: Application to Laser Powder Bed Fusion Process provides comprehensive coverage on the latest methodology in additive manufacturing (AM) modeling and simulation. Although there are extensive advances within the AM field, challenges to predictive theoretical and computational approaches still hinder the widespread adoption of AM. The book reviews metal additive materials and processes and discusses multiscale/multiphysics modeling strategies. In addition, coverage of modeling and simulation of AM process in order to understand the process-structure-property relationship is reviewed, along with the modeling of morphology evolution, phase transformation, and defect formation in AM parts. Residual stress, distortion, plasticity/damage in AM parts are also considered, with scales associated with the spatial, temporal and/or material domains reviewed. This book is useful for graduate students, engineers and professionals working on AM materials, equipment, process, development and modeling. Includes the fundamental principles of additive manufacturing modeling techniques Presents various modeling tools/software for AM modeling Discusses various design methods and how to optimize the AM process using these models
Quality Analysis of Additively Manufactured Metals: Simulation Approaches, Processes, and Microstructure Properties provides readers with a firm understanding of the failure and fatigue processes of additively manufactured metals. With a focus on computational methods, the book analyzes the process-microstructure-property relationship of these metals and how it affects their quality while also providing numerical, analytical, and experimental data for material design and investigation optimization. It outlines basic additive manufacturing processes for metals, strategies for modeling the microstructural features of metals and how these features differ based on the manufacturing process, and more.Improvement of additively manufactured metals through predictive simulation methods and microdamage and micro-failure in quasi-static and cyclic loading scenarios are covered, as are topology optimization methods and residual stress analysis techniques. The book concludes with a section featuring case studies looking at additively manufactured metals in automotive, biomedical and aerospace settings. Provides insights and outlines techniques for analyzing why additively manufactured metals fail and strategies for avoiding those failures Defines key terms and concepts related to the failure analysis, quality assurance and optimization processes of additively manufactured metals Includes simulation results, experimental data and case studies
This work introduces numerical frameworks that enable the prediction of residual stress (RS), distortion, and microstructure from the metal additive manufacturing (AM) process, to reveal new insights that offer a deeper understanding towards the influence these factors have on RS and distortion induced during subsequent post-process operations. Electron backscatter diffraction (EBSD) imaging reported in the literature has provided evidence of microstructural inhomogeneity in metal AM parts that can strongly influence the resultant anisotropic mechanical response. Unfortunately, EBSD imaging only provides 2D observations of microstructure, and hence assumptions regarding the out-of-plane size and shape of individual grains have to be made. While multiple 0́−slices0́+ of EBSD images can be digitally stitched together, such experimental procedures would be very time-intensive, especially for larger AM builds. Over the past decade, numerous 3D microstructure modeling techniques have emerged and/or evolved to address this difficulty. One such approach involves the Kinetic Monte Carlo (KMC) method. A limitation of the existing KMC method, however, is that its modeling technique only allows for static melt pool and heat affected zone; and it thereby neglects important effects of transient thermal history in metal AM processes. Aside from microstructure, considering the thermomechanical nature of metal AM, rapid thermal cycles can cause large magnitudes of RS and distortion to develop within a fused part during the build. Prior investigations documented in the literature report experimental measurement of tensile residual stresses (TRS) in the bulk AM material, along with several types of surface defects. While TRS can result in poor fatigue life of a component, excessive distortions can lead to part rejection or necessitate expensive and time-consuming post-process correction. It should be noted, however, that the preliminary experimental measurement and characterization of RS via techniques such as slitting, x-ray and/or neutron diffraction are either extremely time consuming, costly, or possess a considerable degree of volumetric averaging. Nonetheless, a poor understanding of the RS fields, distortion, and mechanical response of a metal AM part will adversely influence how the part is post-processed. In addition, the final part geometry may not conform to dimensional requirements or possess the load bearing capacity for the desired application. The foregoing issues motivate the need for a physics-based numerical approach by which the AM microstructure, as well as RS and distortion, can be suitably predicted based on the AM process parameters. Furthermore, such a physics-based model may be of great value in assessing the influence of the initial RS and distortion in the AM part on the subsequent RS and distortion that is induced during post-processing operations such as machining or laser shock peening. In this work, several numerical frameworks are presented and deployed to test hypotheses related to the influences of metal AM RS and inhomogeneous microstructure. First, a Dynamic Kinetic Monte Carlo (DKMC) microstructure prediction framework is developed to capture interlayer and intralayer heat accumulation effects when predicting metal AM microstructure. Unlike the existing KMC approach, the DKMC method captures the influence of the AM process parameter dependent transient thermal history on the printed structure0́9s grain morphology. This is followed by a study that incorporates the 3D inhomogeneous microstructure for AM metal, predicted via DKMC, in post-process simulations of micromilling as well as laser shock peening (LSP). The work illuminates key insights into how the 3D microstructure consideration influences material response during post-process operations, and it effectively demonstrates a process-structure-property relationship. An investigation into how the initial RS in the bulk AM material influences the post-process induced RS and distortion is subsequently presented with a high-speed machining case study. Furthermore, the extent by which the machining strategy affects the degree of influence of initial RS on the machining-induced RS and distortion is also investigated. The study offers a comprehensive understanding towards the importance of inclusion of initial RS in the AM bulk material when simulating post-process operations. While the aforementioned studies either focus on the effects of initial RS in the AM bulk material or microstructure, they do not combine the two. Hence, an additional study implementing both metal AM microstructure modeling and its initial RS fields is also presented. A parametric examination on the influence of initial RS fields, microstructure, and the printing environment temperature when applying interlayer burnishing during a laser powder bed fusion process reveals new insights regarding their combined effect. Finally, a research application study is presented which demonstrates how numerical prediction of the vertical distortion along the upper surface of the AM build can be used to devise an in-situ LSP strategy to correct for excessive amounts of such surface distortion. While the frameworks presented in this research are implemented using selective laser melting case studies, they are readily extensible to other powder bed fusion metal AM methods, as well as directed energy deposition and binder jetting technologies. New insights from the tools developed in this research facilitate improved understanding through more realistic predictions of residual stress, distortion, and mechanical response of the AM bulk material when subject to post-process treatments.
Fundamentals of Multiscale Modeling of Structural Materials provides a robust introduction to the computational tools, underlying theory, practical applications, and governing physical phenomena necessary to simulate and understand a wide-range of structural materials at multiple time and length scales. The book offers practical guidelines for modeling common structural materials with well-established techniques, outlining detailed modeling approaches for calculating and analyzing mechanical, thermal and transport properties of various structural materials such as metals, cement/concrete, polymers, composites, wood, thin films, and more.Computational approaches based on artificial intelligence and machine learning methods as complementary tools to the physics-based multiscale techniques are discussed as are modeling techniques for additively manufactured structural materials. Special attention is paid to how these methods can be used to develop the next generation of sustainable, resilient and environmentally-friendly structural materials, with a specific emphasis on bridging the atomistic and continuum modeling scales for these materials. Synthesizes the latest cutting-edge computational multiscale modeling techniques for an array of structural materials Emphasizes the foundations of the field and offers practical guidelines for modeling material systems with well-established techniques Covers methods for calculating and analyzing mechanical, thermal and transport properties of various structural materials such as metals, cement/concrete, polymers, composites, wood, and more Highlights underlying theory, emerging areas, future directions and various applications of the modeling methods covered Discusses the integration of multiscale modeling and artificial intelligence
Microstructure evolution in metal additive manufacturing (AM) is a complex multi-physics and multi-scale problem. Understanding the impact of AM process conditions on the microstructure evolution and the resulting mechanical properties of the printed part is an active area of research. The investigation into understanding the microstructure evolution under AM conditions, at different length scales, is done as a three-part research program that is presented in this thesis. In the first part, a high-fidelity numerical method at the mesoscale to model varied dendritic solidification morphologies is developed. A numerical framework encompassing the modeling of Stefan problem formulations relevant to dendritic evolution using a phase-field approach and a finite element method implementation is presented. Using this framework, numerous complex dendritic morphologies that are physically relevant to the solidification of pure melts and binary alloys are modeled. To the best of our knowledge, this is a first-of-its-kind study of numerical convergence of the phase-field equations of dendritic growth in a finite element method setting. Further, using this numerical framework, various types of physically relevant dendritic solidification patterns like single equiaxed, multi-equiaxed, single-columnar, and multi-columnar dendrites are modeled in two-dimensional and three-dimensional computational domains. In the second part, the complex dynamics of meltpool formation during metal additive manufacturing are modeled using a thermo-fluidic numerical model. Statistical-based method of least-squares is exploited to characterize the role of dimensional numbers in the microstructure evolution process. A novel strategy using dimensional analysis and the method of linear least-squares regression to numerically investigate the thermo-fluidic governing equations of the Laser Powder Bed Fusion AM process is presented. First, the governing equations are solved using the finite element method, and the model predictions are validated with experimental and numerical results from the literature. Then, through dimensional analysis, an important dimensionless quantity - interpreted as a measure of heat absorbed by the powdered material and the meltpool, is identified. Key contributions of this work include the demonstration of the correlation between the dimensionless measure of heat absorbed, and classical dimensionless quantities such as Peclet, Marangoni, and Stefan numbers, with advective transport in the meltpool for different alloys, meltpool morphologies, and microstructure evolution-related variables In the third part, the influence on the morphology of evolving dendritic microstructure due to the rapid thermal cycle and fluid convection in the meltpool during metal additive manufacturing is investigated. A finite-element formulation that solves a coupled Navier-Stokes flow model and a phase-field model of dendritic solidification is developed. Microstructure evolution modeled using purely heat and mass diffusion process may not capture the entire spectrum of the dendrite morphology observed in metal additive manufacturing. The impact of flow dynamics on the thermal gradients and momentum transfer that modulate dendritic shapes, along with the associated remelting are modeled using a coupled phase-field model of solidification. Further, the morphological changes to dendrites in the solidifying region beneath the meltpool fusion line are modeled by accounting for convective effects in the mass and heat diffusion process in equiaxed, aligned equiaxed, and columnar dendrite growth for a pure metal and binary alloys. It is observed that for a meltpool formed under high laser power and scan speed conditions, where Marangoni convection is significant, enhanced growth of the secondary arms of columnar dendrite occurs as compared to dendrite growth observed in low convection regions of the meltpool.
Additive manufacturing (AM) of metals and composites using laser energy, direct energy deposition, electron beam methods, and wire arc melting have recently gained importance due to their advantages in fabricating the complex structure. Today, it has become possible to reliably manufacture dense parts with certain AM processes for many materials, including steels, aluminum and titanium alloys, superalloys, metal-based composites, and ceramic matrix composites. In the near future, the AM material variety will most likely grow further, with high-performance materials such as intermetallic compounds and high entropy alloys already under investigation. Additive Manufacturing Applications for Metals and Composites is a pivotal reference source that provides vital research on advancing methods and technological developments within additive manufacturing practices. Special attention is paid to the material design of additive manufacturing of parts, the choice of feedstock materials, the metallurgical behavior and synthesis principle during the manufacturing process, and the resulted microstructures and properties, as well as the relationship between these factors. While highlighting topics such as numerical modeling, intermetallic compounds, and statistical techniques, this publication is ideally designed for students, engineers, researchers, manufacturers, technologists, academicians, practitioners, scholars, and educators.
Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.
Fatigue in Additive Manufactured Metals provides a brief overview of the fundamental mechanics involved in metal fatigue and fracture, assesses the unique properties of additive manufactured metals, and provides an in-depth exploration of how and why fatigue occurs in additive manufactured metals. Additional sections cover solutions for preventing it, best-practice design methods, and more. The book recommends cutting-edge evidence-based approaches for designing longer lasting additive manufactured metals, discusses the latest trends in the field and the various aspects of low cycle fatigue, and looks at both post-treatment and manufacturing process-based solutions. By providing international standards and testing procedures of additive manufactured metal parts and discussing the environmental impacts of additive manufacturing of metals and outlining simulation and modeling scenarios, this book is an ideal resource for users in industry. Discusses the underlying mechanisms controlling the fatigue behavior of additive manufactured metal components as well as how to improve the fatigue life of these components via both manufacturing processes and post-processing Studies the variability of properties in additive manufactured metals, the effects of different process conditions on mechanical reliability, probabilistic versus deterministic aspects, and more Outlines nondestructive failure analysis techniques and highlights the effects of unique microstructural characteristics on fatigue in additive manufactured metals
Metal additive manufacturing is an enabling technology for the rapid prototyping and manufacturing of geometrically complex parts that would otherwise be difficult or impossible to manufacture. However, the manufacturing process can produce undesired residual stresses and distortions. The first part of the work describes the implementation of a multiscale, thermo-mechanical simulation modeling the metal powder bed fusion additive manufacturing process. NASA’s Micromechanics Analysis Code was is to incorporate the microscale effects of an evolving material porosity on the predicted macroscale residual fields. The simulation shows that modeling an evolving material porosity, as the material transitions from a metal powder to a solid, significantly affects the magnitude of the residual stresses and distortions, compared to a constant porosity model. The second part of this work uses the developed simulations to assess the effects of geometrical features. A linear regression shows that there is a correlation between the residual fields and the geometry. This suggests that it may be feasible to predictably influence the residual fields by modifying the geometry. This work is part of a larger work aimed at optimizing the geometry to minimize the residual stresses and distortions.
