Multiscale Topology Optimization for Additively Manufactured Objects

2018 ◽  
Vol 18 (3) ◽  
Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Topology Optimization ◽  
Energy Deposition ◽  
Multiple Scales ◽  
Precise Control ◽  
Hand Tool ◽  
Macro Scale ◽  
Performance Specifications ◽  
Future Work ◽  
Am Processes ◽  
Meso Scale

The precise control of mass and energy deposition associated with additive manufacturing (AM) processes enables the topological specification and realization of how space can be filled by material in multiple scales. Consequently, AM can be pursued in a manner that is optimized such that fabricated objects can best realize performance specifications. In the present work, we propose a computational multiscale method that utilizes the unique meso-scale structuring capabilities of implicit slicers for AM, in conjunction with existing topology optimization (TO) tools for the macro-scale, in order to generate structurally optimized components. The use of this method is demonstrated on two example objects including a load bearing bracket and a hand tool. This paper also includes discussion concerning the applications of this methodology, its current limitations, a recasting of the AM digital thread, and the future work required to enable its widespread use.

Towards Multiscale Topology Optimization for Additively Manufactured Components Using Implicit Slicing

2017 ◽  
Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos
Keyword(s):  
Topology Optimization ◽  
Complex Geometries ◽  
Generate Functional ◽  
Hand Tool ◽  
Macro Scale ◽  
Functional Features ◽  
Future Work ◽  
Functional Components ◽  
Am Processes ◽  
Meso Scale

Additive Manufacturing (AM) encompasses a set of fabrication technologies that are being used with increasing frequency in a wide variety of scientific and industrial pursuits. These technologies, which operate by successive additions of material to a domain, enable the manufacture of highly complex geometries that would otherwise be unrealizable. However, the material micro and meso-structures generated by AM processes differ remarkably from those that arise from conventional techniques and occasionally introduce unwanted functional features; this has been an obstacle to the use of AM in some applications. In the present work, we propose a multiscale method that utilizes the unique meso-scale structuring capabilities of implicit slicers for AM, in conjunction with existing topology optimization tools for the macro-scale, in order to generate functional components. The use of this method is demonstrated on the example of a hand tool. We discuss the applications of this methodology, its current limitations, and the future work required to enable its widespread use.

Functional Performance Tailoring of Additively Manufactured Components via Topology Optimization

2017 ◽  
Author(s):  
John C. Steuben ◽  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
Andrew J. Birnbaum
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
High Performance ◽  
Functional Performance ◽  
Underwater Vehicle ◽  
Length Scale ◽  
Hand Tool ◽  
Component Shape ◽  
Future Work ◽  
Am Processes

The freedom of design that is afforded by Additive Manufacturing (AM) processes opens exciting possibilities for the production of lightweight, high performance components and structures. Consequently, in recent years the development of software tools to enable engineering design methods that exploit the unique features of AM has become a subject of increased research interest. In this paper we explore the use of Topology Optimization (TO) algorithms to tailor component shape in order to achieve the intended functionality of additively manufactured components at the macro length scale. We present two case studies: the first concerns the hierarchical nesting of functions in a hand tool, while the second covers the development of a metamaterial component substructure for an Uninhabited Underwater Vehicle (UUV) hull. We offer conclusions regarding the usefulness of TO techniques for the design of AM components, and a summary of future work, which we feel is necessary to improve such methodologies.

Meso-Scale Experimental & Numerical Studies for Predicting Macro-scale Performance of Advanced Reactive Materials (ARMs)

2015 ◽  
Author(s):  
Naresh Thadhani ◽  
Arun Gokhale ◽  
Jason Quenneville ◽  
Jennifer Breidenich ◽  
Manny Gonzales ◽  
...  
Keyword(s):  
Reactive Materials ◽  
Numerical Studies ◽  
Macro Scale ◽  
Meso Scale

scholarly journals Substrate design to minimize residual stresses in Directed Energy Deposition AM processes

2021 ◽  
Vol 202 ◽  
pp. 109525
Author(s):  
Xufei Lu ◽  
Michele Chiumenti ◽  
Miguel Cervera ◽  
Junjie Li ◽  
Xin Lin ◽  
...  
Keyword(s):  
Residual Stresses ◽  
Energy Deposition ◽  
Directed Energy Deposition ◽  
Directed Energy ◽  
Am Processes

scholarly journals A Shape Optimization Method for Part Design Derived from the Buildability Restrictions of the Directed Energy Deposition Additive Manufacturing Process

Designs ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 19
Author(s):  
Andreas K. Lianos ◽  
Harry Bikas ◽  
Panagiotis Stavropoulos
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Energy Deposition ◽  
Design Method ◽  
Current Trend ◽  
Internal Stresses ◽  
Material Distribution ◽  
Load Case ◽  
Design Elements ◽  
Industrial Sectors

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Paul Witherell ◽  
Shaw Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Metal Powder ◽  
Manufacturing Process ◽  
Process Model ◽  
Model Development ◽  
Process Models ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Future Work ◽  
Am Processes

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process 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.

Permeability Prediction in the Impregnation Stage of Resin Transfer Moulding

2010 ◽  
Vol 160-162 ◽  
pp. 1211-1216
Author(s):  
Zhuang Liu ◽  
Xiao Qing Wu
Keyword(s):  
Stokes Equations ◽  
Approximate Model ◽  
Local Deformation ◽  
Resin Transfer Moulding ◽  
Moulding Process ◽  
2D Simulation ◽  
Macro Scale ◽  
Darcy Equations ◽  
Transfer Moulding ◽  
Meso Scale

The impregnation stage of the Resin Transfer Moulding process can be simulated by solving the Darcy equations on a mould model, with a ‘macro-scale’ finite element method. For every element, a local ‘meso-scale’ permeability must be determined, taking into account the local deformation of the textile reinforcement. This paper demonstrates that the meso-scale permeability can be computed efficiently and accurately by using meso-scale simulation tools. We discuss the speed and accuracy requirements dictated by the macro-scale simulations. We show that these requirements can be achieved for two meso-scale simulators, coupled with a geometrical textile reinforcement modeller. The first solver is based on a finite difference discretisation of the Stokes equations, the second uses an approximate model, based on a 2D simulation of the flow.

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.

scholarly journals Macro-scale topology optimization for controlling internal shear stress in a porous scaffold bioreactor

2012 ◽  
Vol 109 (7) ◽  
pp. 1844-1854 ◽  
Author(s):  
K. Youssef ◽  
J.J. Mack ◽  
M.L. Iruela-Arispe ◽  
L.-S. Bouchard
Keyword(s):  
Shear Stress ◽  
Topology Optimization ◽  
Porous Scaffold ◽  
Macro Scale

scholarly journals Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

2019 ◽  
Author(s):  
Milou Straathof ◽  
Michel R.T. Sinke ◽  
Theresia J.M. Roelofs ◽  
Erwin L.A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Rat Brain ◽  
Resting State ◽  
Structural Connectivity ◽  
Distinct Structure ◽  
Macro Scale ◽  
Function Relationship ◽  
The Brain ◽  
Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

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