Enriched Analytical Solutions for Additive Manufacturing Modeling and Simulation

2018 ◽  
Author(s):  
John C. Steuben ◽  
Andrew J. Birnbaum ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Additive Manufacturing ◽  
Modeling And Simulation ◽  
Analytical Solutions ◽  
Analytic Solutions ◽  
Computational Effort ◽  
Infinite Domain ◽  
Underlying Assumption ◽  
Simulation Tools ◽  
Computational Performance ◽  
Am Processes

Additive Manufacturing (AM) is an increasingly widespread family of technologies for the fabrication of objects based on successive depositions of mass and energy. A strong need for modeling and simulation tools for AM exists, in order to predict thermal histories, residual stresses, microstructure, and various other aspects of the resulting components. In this paper we explore the use of analytic solutions to model the thermal aspects of AM, in an effort to achieve high computational performance and enable “in the loop” use for feedback control of AM processes. It is shown that the utility of existing analytical solutions is limited due to their underlying assumption of a homogeneous semi-infinite domain. These solutions must therefore be enriched from their exact form in order to capture the relevant thermal physics associated with AM processes. Such enrichments include the handling of strong nonlinear variations in material properties, finite non-convex solution domains, behavior of heat sources very near boundaries, and mass accretion coupled to the thermal problem. The enriched analytic solution method (EASM) is shown to produce results equivalent to those of numerical methods which require six orders of magnitude greater computational effort.

Toward Feedback Control for Additive Manufacturing Processes Via Enriched Analytical Solutions

2019 ◽  
Vol 19 (3) ◽  
Author(s):  
John C. Steuben ◽  
Andrew J. Birnbaum ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Additive Manufacturing ◽  
Feedback Control ◽  
Analytical Solutions ◽  
Analytic Solutions ◽  
Computational Effort ◽  
Infinite Domain ◽  
Underlying Assumption ◽  
Computational Performance ◽  
Process Feedback ◽  
Am Processes

Additive manufacturing (AM) enables the fabrication of objects using successive additions of mass and energy. In this paper, we explore the use of analytic solutions to model the thermal aspects of AM, in an effort to achieve high computational performance and enable “in the loop” use for feedback control of AM processes. It is shown that the utility of existing analytical solutions is limited due to their underlying assumption of a homogeneous semi-infinite domain. These solutions must, therefore, be enriched from their exact form in order to capture the relevant thermal physics associated with AM processes. Such enrichments include the handling of strong nonlinear variations in material properties, finite nonconvex solution domains, behavior of heat sources very near boundaries, and mass accretion coupled to the thermal problem. The enriched analytic solution method (EASM) is shown to produce results equivalent to those of numerical methods, which require six orders of magnitude greater computational effort. It is also shown that the EASM's computational performance is sufficient to enable AM process feedback control.

Recent Developments of the Multiphysics Discrete Element Method for Additive Manufacturing Modeling and Simulation

2017 ◽  
Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Additive Manufacturing ◽  
Discrete Element Method ◽  
Discrete Element ◽  
Additive Manufacture ◽  
Computational Tools ◽  
Computational Performance ◽  
Macro Scale ◽  
Recent Developments ◽  
Am Processes ◽  
Element Method

Recent years have seen a sharp increase in the development and usage of Additive Manufacturing (AM) technologies for a broad range of scientific and industrial purposes. The drastic microstructural differences between materials produced via AM and conventional methods has motivated the development of computational tools that model and simulate AM processes in order to facilitate their control for the purpose of optimizing the desired outcomes. This paper discusses recent advances in the continuing development of the Multiphysics Discrete Element Method (MDEM) for the simulation of AM processes. This particle-based method elegantly encapsulates the relevant physics of powder-based AM processes. In particular, the enrichment of the underlying constitutive behaviors to include thermoplasticity is discussed, as are methodologies for modeling the melting and re-solidification of the feedstock materials. Algorithmic improvements that increase computational performance are also discussed. The MDEM is demonstrated to enable the simulation of the additive manufacture of macro-scale components. Concluding remarks are given on the tasks required for the future development of the MDEM, and the topic of experimental validation is also discussed.

Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models

2014 ◽  
Author(s):  
Paul Witherell ◽  
Shaw C. Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Modeling And Simulation ◽  
Model Development ◽  
Process Models ◽  
Loop Control ◽  
Powder Bed ◽  
Potential Benefits ◽  
Future Work ◽  
Application Specific ◽  
Am Processes

Though the advanced manufacturing capabilities offered by additive manufacturing (AM) have been known for several decades, industry adoption of AM technologies has been relatively slow. Recent advances in modeling and simulation of AM processes and materials are providing new insights to help overcome some of the barriers that have hindered adoption. However, these models and simulations are often application specific, and few are developed in an easily reusable manner. Variations are compounded because many models are developed as independent or proprietary efforts, and input and output definitions have not been standardized. To further realize the potential benefits of modeling and simulation advancements, including predictive modeling and closed-loop control, more coordinated efforts must be undertaken. In this paper, we advocate a more harmonized approach to model development, through classification and metamodeling that will support model composability, reusability, and integration. We review several types of AM models and use direct metal powder bed fusion characteristics to provide illustrative examples of the proposed classification and metamodel approach. We describe how a coordinated approach can be used to extend modeling capabilities by promoting model composability. As part of future work, a framework is envisioned to realize a more coherent strategy for model development and deployment.

Enriched analytical solutions for additive manufacturing modeling and simulation

2019 ◽  
Vol 25 ◽  
pp. 437-447 ◽  
Author(s):  
John C. Steuben ◽  
Andrew J. Birnbaum ◽  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos
Keyword(s):  
Additive Manufacturing ◽  
Modeling And Simulation ◽  
Analytical Solutions

Phase Transformation Advancements of the Enriched Analytic Solution Method for Additive Manufacturing Applications

2019 ◽  
Author(s):  
John C. Steuben ◽  
Andrew J. Birnbaum ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Finite Element ◽  
Phase Transformation ◽  
Additive Manufacturing ◽  
Analytic Solution ◽  
Analytic Solutions ◽  
Element Discretization ◽  
Simulation Tools ◽  
Property Data ◽  
Manufacturing Applications ◽  
Melting And Solidification

Abstract Renewed interest in additive manufacturing (AM) and rapid prototyping technologies has driven great demand for corresponding modeling and simulation tools. While most such models are defined via the finite-element discretization of the relevant multi-physics, the authors have recently developed a method based on the enrichment of classical analytic solutions to the heat equation. The principal advantage of this enriched analytic solution methodology (EASM) is its high computational efficiency that can enable in-the-loop process control in a manner that removes assumptions made for classic analytical solutions and accounts for additional physics. These features enable the efficient and accurate exploration of the high-dimensional AM process parameter space. This work presents a further enrichment of the underlying analytic solutions to include the effects of phase transformation upon melting and solidification, which are shown to be significant in magnitude. It is demonstrated that the available property data for common AM materials are not adequate for accurate thermal modeling (via finite-element, EASM, or other means), and must be improved via future experimental efforts. A discussion of the accuracy and significance of the results achieved, and a summary of further work necessary to bring the EASM to maturity concludes this work.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

2021 ◽  
Author(s):  
M. A. Millán ◽  
R. Galindo ◽  
A. Alencar
Keyword(s):  
Bearing Capacity ◽  
Analytical Solutions ◽  
Shear Failure ◽  
Numerical Models ◽  
Computational Effort ◽  
Shallow Foundations ◽  
Geological Strength Index ◽  
Rock Masses ◽  
Ann Model ◽  
Artificial Neural

AbstractCalculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

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