scholarly journals Topology optimization subject to additive manufacturing constraints

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moritz Ebeling-Rump ◽  
Dietmar Hömberg ◽  
Robert Lasarzik ◽  
Thomas Petzold
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Computational Cost ◽  
Ginzburg Landau ◽  
First Order ◽  
Manufacturing Constraint ◽  
External Forces ◽  
The Cost ◽  
Low Computational Cost ◽  
The Ideal

AbstractIn topology optimization the goal is to find the ideal material distribution in a domain subject to external forces. The structure is optimal if it has the highest possible stiffness. A volume constraint ensures filigree structures, which are regulated via a Ginzburg–Landau term. During 3D printing overhangs lead to instabilities. As a remedy an additive manufacturing constraint is added to the cost functional. First order optimality conditions are derived using a formal Lagrangian approach. With an Allen-Cahn interface propagation the optimization problem is solved iteratively. At a low computational cost the additive manufacturing constraint brings about support structures, which can be fine tuned according to demands and increase stability during the printing process.

scholarly journals Data-Driven Additive Manufacturing Constraints for Topology Optimization

2018 ◽  
Author(s):  
Benjamin M. Weiss ◽  
Joshua M. Hamel ◽  
Mark A. Ganter ◽  
Duane W. Storti
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimal Designs ◽  
Data Driven ◽  
Constraint Function ◽  
Manufacturing Constraint ◽  
Gradient Based ◽  
Fixed Radius ◽  
Different Shapes ◽  
Moving Morphable Components

The topology optimization (TO) of structures to be produced using additive manufacturing (AM) is explored using a data-driven constraint function that predicts the minimum producible size of small features in different shapes and orientations. This shape- and orientation-dependent manufacturing constraint, derived from experimental data, is implemented within a TO framework using a modified version of the Moving Morphable Components (MMC) approach. Because the analytic constraint function is fully differentiable, gradient-based optimization can be used. The MMC approach is extended in this work to include a “bootstrapping” step, which provides initial component layouts to the MMC algorithm based on intermediate Solid Isotropic Material with Penalization (SIMP) topology optimization results. This “bootstrapping” approach improves convergence compared to reference MMC implementations. Results from two compliance design optimization example problems demonstrate the successful integration of the manufacturability constraint in the MMC approach, and the optimal designs produced show minor changes in topology and shape compared to designs produced using fixed-radius filters in the traditional SIMP approach. The use of this data-driven manufacturability constraint makes it possible to take better advantage of the achievable complexity in additive manufacturing processes, while resulting in typical penalties to the design objective function of around only 2% when compared to the unconstrained case.

scholarly journals A Methodology to Evaluate the Reliability Impact of the Replacement of Welded Components by Additive Manufacturing Spare Parts

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 932 ◽  
Author(s):  
Armando Coro ◽  
Luis María Macareno ◽  
Josu Aguirrebeitia ◽  
Luis Norberto López de Lacalle
Keyword(s):  
Additive Manufacturing ◽  
Computational Cost ◽  
Reliability Function ◽  
Spare Parts ◽  
Jet Engine ◽  
First Order ◽  
Propagation Analysis ◽  
Reliability Method ◽  
And Performance ◽  
Inspection Scheduling

This article shows a method for inspection scheduling of structures made by additive manufacturing, derived from reliability function evaluations and overhaul inspection findings. The routine was an adaption of an existing method developed by the authors for welded components; in this latter case, the routine used a stochastic defect-propagation analysis for pores and lack of fusion defects of additive manufacturing process, instead of the weld liquation crack. In addition, the authors modified the specific stress-intensity factor for welded components to consider additive manufacturing-related material property variability, defect distributions, flaw-inspection capabilities, and component geometry. The proposed routine evaluated the failure rate and inspection intervals using the first-order reliability method (FORM + Fracture) to alleviate the computational cost of probabilistic defect-propagation analysis. The proposed method is one of the first applying reliability concepts to additive manufacturing (AM) components. This is an important milestone, since in 10 years, additive manufacturing is to be used for 30% of the components in aeroengines. This paper presents an example comparing the reliability and cost of a jet engine, with components either made by additive manufacturing or welded parts; in the process, the reliability AM-key features are found, and overhaul schedules of an airplane fleet made with AM components are defined. The simplicity and performance demonstrated in the comparison make the proposed method a powerful engineering tool for additive manufacturing assessment in aeronautics.

scholarly journals Data-Driven Additive Manufacturing Constraints for Topology Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Benjamin M. Weiss ◽  
Joshua M. Hamel ◽  
Mark A. Ganter ◽  
Duane W. Storti
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimal Designs ◽  
Data Driven ◽  
Constraint Function ◽  
Manufacturing Constraint ◽  
Gradient Based ◽  
Fixed Radius ◽  
Different Shapes ◽  
Moving Morphable Components

Abstract The topology optimization (TO) of structures to be produced using additive manufacturing (AM) is explored using a data-driven constraint function that predicts the minimum producible size of small features in different shapes and orientations. This shape- and orientation-dependent manufacturing constraint, derived from experimental data, is implemented within a TO framework using a modified version of the moving morphable components (MMC) approach. Because the analytic constraint function is fully differentiable, gradient-based optimization can be used. The MMC approach is extended in this work to include a “bootstrapping” step, which provides initial component layouts to the MMC algorithm based on intermediate solid isotropic material with penalization (SIMP) topology optimization results. This “bootstrapping” approach improves convergence compared with reference MMC implementations. Results from two compliance design optimization example problems demonstrate the successful integration of the manufacturability constraint in the MMC approach, and the optimal designs produced show minor changes in topology and shape compared to designs produced using fixed-radius filters in the traditional SIMP approach. The use of this data-driven manufacturability constraint makes it possible to take better advantage of the achievable complexity in additive manufacturing processes, while resulting in typical penalties to the design objective function of around only 2% when compared with the unconstrained case.

Laminated-Object-Manufacturing*/Laminated object manufacturing - A method for the production of topological optimization parts

2016 ◽  
Vol 106 (05) ◽  
pp. 354-359
Author(s):  
M. Mottahedi ◽  
P. Zahn ◽  
A. Lechler ◽  
A, Prof. Verl
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Topological Optimization ◽  
Optimal Topology ◽  
Manufacturing Method ◽  
Laminated Object Manufacturing ◽  
Maximum Stiffness ◽  
Global Topology ◽  
The Cost

Topologisch optimierte Bauteile gestatten maximale Steifigkeit bei minimalem Materialeinsatz. Für die Erzeugung solcher Topologien werden meist Algorithmen eingesetzt, die Fertigungseinschränkungen auf Kosten von optimalen Ergebnissen berücksichtigen und keine variablen Materialdichten zulassen. Dieser Fachartikel stellt ein additives Herstellungsverfahren zur Fertigung global optimaler Topologien vor. Als Ergebnis können mittels der ausgewählten Algorithmen Bauteile mit höherer Steifigkeit hergestellt werden.   The optimal topology of components leads to maximum stiffness with minimum material use. To generate these topologies, normally algorithms are employed that tackle manufacturing limitations at the cost of the optimum. This article introduces an additive manufacturing method to enable the production of global topology optimization results. The findings show that by implementing the selected algorithm the stiffness of the components are higher than what could have been produced by conventional techniques.

Multi-Material Topology Optimization for Additive Manufacturing

2015 ◽  
Author(s):  
Amir M. Mirzendehdel ◽  
Krishnan Suresh
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Methods ◽  
Optimal Designs ◽  
Element Analysis ◽  
First Order ◽  
Multiple Materials ◽  
Material Fabrication ◽  
Single Material ◽  
Order Element

Additive manufacturing (AM) and topology optimization strongly complement each other in that the complex and optimal designs created through the latter can directly be fabricated through AM, leading to reduced design and fabrication time. As AM expands into multi-material fabrication, there is a natural need for efficient multi-material topology optimization methods, where one must simultaneously optimize the topology, and the distribution of various materials within the topology. In this paper we generalize the single-material Pareto tracing method of topology optimization to multiple materials, and discuss its implementation using assembly-free finite element analysis, and first-order element-sensitivity. The effectiveness of the algorithm is demonstrated through illustrative examples.

scholarly journals Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2239
Author(s):  
Mriganka Roy ◽  
Olga Wodo
Keyword(s):  
Additive Manufacturing ◽  
Tool Path ◽  
Computational Cost ◽  
Deposition Process ◽  
Surrogate Models ◽  
High Accuracy ◽  
Medium Size ◽  
Fused Filament Fabrication ◽  
The Cost ◽  
Consolidation Degree

Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model).

Minimization of Cost and Print Time of Additive Manufacturing via Topology Optimization

2018 ◽  
Author(s):  
Graeme Sabiston ◽  
Luke Ryan ◽  
Il Yong Kim
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Surface Area ◽  
Large Scale ◽  
Time Factors ◽  
Design Tool ◽  
Support Material ◽  
Manufacturing Operations ◽  
Volume Yield ◽  
The Cost

As the field of design for additive manufacturing continues to evolve and accelerate towards admitting more robust designs requiring fewer instances of user-intervention, we will see the conventional design cycle evolve dramatically. However, to fully take advantage of this emerging technology — particularly with respect to large scale manufacturing operations — considerations of productivity from a fiscal perspective are sure to become of the utmost importance. A mathematical model incorporating the cost and time factors associated with additive manufacturing processes has been developed and implemented as a multi-weighted single-objective topology optimization algorithm. The aforementioned factors have been identified as component surface area and volume of support material. These quantities are optimized alongside compliance, producing a design tool that gives the user the option to choose the relative weighting of performance over cost. In two academic examples, minimization of compliance alongside surface area and support structure volume yield geometries demonstrating that considerable decreases in support material in particular can be achieved without sacrificing significant part compliance.

Parametric Topology Optimization Toward Rational Design and Efficient Prefabrication for Additive Manufacturing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Long Jiang ◽  
Hang Ye ◽  
Chi Zhou ◽  
Shikui Chen
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Level Set ◽  
Rational Design ◽  
Computational Cost ◽  
Significant Advance ◽  
Free Form ◽  
Geometric Complexity ◽  
Original Design ◽  
Design And Manufacturing

The significant advance in the boosted fabrication speed and printing resolution of additive manufacturing (AM) technology has considerably increased the capability of achieving product designs with high geometric complexity and provided tremendous potential for mass customization. However, it is also because of geometric complexity and large quantity of mass-customized products that the prefabrication (layer slicing, path planning, and support generation) is becoming the bottleneck of the AM process due to the ever-increasing computational cost. In this paper, the authors devise an integrated computational framework by synthesizing the parametric level set-based topology optimization method with the stereolithography (SLA)-based AM process for intelligent design and manufacturing of multiscale structures. The topology of the design is optimized with a distance-regularized parametric level set method considering the prefabrication computation. With the proposed framework, the structural topology optimization not only can create single material structure designs but also can generate multiscale, multimaterial structures, offering the flexibility and robustness of the structural design that the conventional methods could not provide. The output of the framework is a set of mask images that can be directly used in the AM process. The proposed approach seamlessly integrates the rational design and manufacturing to reduce the numerical complexity of the computationally expensive prefabrication process. More specifically, the prefabrication-friendly design and optimization procedure are devised to drastically eliminate the redundant computations in the traditional framework. Two test examples, including a free-form 3D cantilever beam and a multiscale meta-structure, are utilized to demonstrate the performance of the proposed approach. Both the simulation and experimental results verified that the new rational design could significantly reduce the prefabrication computation cost without affecting the original design intent or sacrificing the original functionality.

scholarly journals Large-scale simulation of biomembranes: bringing realistic kinetics to coarse-grained models

2019 ◽  
Author(s):  
Mohsen Sadeghi ◽  
Frank Noé
Keyword(s):  
Large Scale ◽  
Stochastic Dynamics ◽  
Computational Cost ◽  
Coarse Grained ◽  
Fluctuation Spectrum ◽  
Biologically Relevant ◽  
Membrane Models ◽  
Kinetic Effects ◽  
The Cost ◽  
Low Computational Cost

Biomembranes are two-dimensional assemblies of phospholipids that are only a few nanometres thick, but form micrometer-sized structures vital to cellular function. Explicit modelling of biologically relevant membrane systems is computationally expensive, especially when the large number of solvent particles and slow membrane kinetics are taken into account. While highly coarse-grained solvent-free models are available to study equilibrium behaviour of membranes, their efficiency comes at the cost of sacrificing realistic kinetics, and thereby the ability to predict pathways and mechanisms of membrane processes. Here, we present a framework for integrating coarse-grained membrane models with anisotropic stochastic dynamics and continuum-based hydrodynamics, allowing us to simulate large biomembrane systems with realistic kinetics at low computational cost. This paves the way for whole-cell simulations that still include nanometer/nanosecond spatiotemporal resolutions. As a demonstration, we obtain and verify fluctuation spectrum of a full-sized human red blood cell in a 150-milliseconds-long single trajectory. We show how the kinetic effects of different cytoplasmic viscosities can be studied with such a simulation, with predictions that agree with single-cell experimental observations.

scholarly journals Video Based Person Re-Identification Through Selective Knowledge Distillation

2019 ◽  
pp. 699-703
Author(s):  
Gudavalli Sai Abhilash ◽  
Kantheti Rajesh ◽  
Jangam Dileep Shaleem ◽  
Grandi Sai Sarath ◽  
Palli R Krishna Prasad
Keyword(s):  
Computational Cost ◽  
Low Complexity ◽  
Fine Tuning ◽  
Low Resolution ◽  
Facial Cues ◽  
Significant Performance ◽  
Knowledge Distillation ◽  
Computational Resources ◽  
The Cost ◽  
Low Computational Cost

The creation and deployment of face recognition models need to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation in live video. In this approach, a two-stream convolution neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine- tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification.

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