Multicomponent Topology Optimization for Additive Manufacturing With Build Volume and Cavity Free Constraints

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Powder Bed ◽  
Optimization Framework ◽  
Minimum Compliance ◽  
Single Piece ◽  
Gradient Based ◽  
Length Width

Topology optimization for additive manufacturing has been limited to the design of single-piece components that fit within the printer's build volume. This paper presents a gradient-based multicomponent topology optimization method for structures assembled from components built by powder bed additive manufacturing (MTO-A), which enables the design of multipiece assemblies larger than the printer's build volume. Constraints on component geometry for powder bed additive manufacturing are incorporated in a density-based topology optimization framework, with an additional design field governing the component partitioning. For each component, constraints on the maximum allowable build volume (i.e., length, width, and height) and the elimination of enclosed cavities are imposed during the simultaneous optimization of the overall topology and component partitioning. Numerical results of the minimum compliance designs revealed that manufacturing constraints, previously applied to single-piece topology optimization, can unlock richer design exploration space when applied to multicomponent designs.

Multi-Component Topology Optimization for Powder Bed Additive Manufacturing (MTO-A)

2018 ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Simultaneous Optimization ◽  
Previous Attempt ◽  
Numerical Instability ◽  
Feature Size ◽  
Powder Bed ◽  
Nonlinear Projection ◽  
Gradient Based ◽  
Length Width

This paper presents a gradient-based multi-component topology optimization (MTO) method for structures assembled from components made by powder bed additive manufacturing. It is built upon our previous work on the continuously-relaxed MTO framework utilizing the concept of fractional component membership. The previous attempt on the integration of the relaxed MTO framework with additive manufacturing constraints, however, suffered from numerical instability for larger size problems, limiting its application to 2D low-resolution examples. To overcome this difficulty, this paper proposes an improved MTO formulation based on a design field regularization and a nonlinear projection of component membership variables, with a focus on powder bed additive manufacturing. For each component, constraints on the maximum allowable build volume (i.e., length, width, and height), the elimination of enclosed voids, and the minimum printable feature size are imposed during the simultaneous optimization of the overall base topology and component partitioning. The scalability of the new MTO formulation is demonstrated by a few 2D examples with much higher resolution than previously reported, and the first reported 3D example of MTO.

Gradient-Based Multi-Component Topology Optimization for Additive Manufacturing (MTO-A)

2017 ◽  
Author(s):  
Yuqing Zhou ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Simultaneous Optimization ◽  
Minimum Length ◽  
Manufacturing Constraints ◽  
Component Level ◽  
Component Size ◽  
Design Variables ◽  
Gradient Based ◽  
Minimum Length Scale

Topology optimization for additive manufacturing has been limited to the component-level designs with the component size smaller than the printer’s build volume. To enable the design of structures larger than the printer’s build volume, this paper presents a gradient-based multi-component topology optimization framework for structures assembled from components built by additive manufacturing. Constraints on component geometry for additive manufacturing are incorporated in the density-based topology optimization, with additional design variables specifying fractional component membership. For each component, constraints on build size, enclosed voids, overhangs, and the minimum length scale are imposed during the simultaneous optimization of overall base topology and component partitioning. The preliminary result on a minimum compliance structure shows promising advantages over the conventional monolithic topology optimization. Manufacturing constraints previously applied to monolithic topology optimization gain new interpretations when applied to multi-component assemblies, which can unlock richer design space for topology exploration.

Topology Optimization Taking Into Account Geometrical Constraint of No-Closed Hole for Additive Manufacturing Based on Fictitious Physical Model Concept

2021 ◽  
Author(s):  
Takayuki Yamada ◽  
Yuki Noguchi
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Physical Model ◽  
Optimization Method ◽  
Geometrical Constraint ◽  
The Finite Element Method ◽  
Model Concept ◽  
Optimization Framework ◽  
Proposed Model ◽  
Topology Optimization Method

Abstract This paper proposes a topology optimization method that considers the geometrical constraint of a non-closed hole for additive manufacturing based on the fictitious physical model concept. First, the basic topology optimization concept and level set-based method are introduced. Second, the concept of a fictitious physical model for geometrical constraint in the topology optimization framework is discussed. Then, the model for the geometrical constraint of a non-closed hole for additive manufacturing is proposed. Numerical examples are provided to validate the proposed model. In addition, topology optimization considering this geometrical constraint is formulated, and topology optimization algorithms are constructed using the finite element method. Finally, optimization examples are provided to validate the proposed topology optimization method.

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.

Non-Probabilistic Based Topology Optimization Under External Load Uncertainty With Eigenvalue-Superposition of Convex Models

2013 ◽  
Author(s):  
Xike Zhao ◽  
Hae Chang Gea ◽  
Wei Song
Keyword(s):  
Topology Optimization ◽  
External Load ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Convex Model ◽  
Structure Response ◽  
Gradient Based ◽  
Bounded Convex ◽  
Topology Optimization Method

In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.

Incorporating Manufacturing Constraints in Topology Optimization Methods: A Survey

2017 ◽  
Author(s):  
Alok Sutradhar ◽  
Jaejong Park ◽  
Payam Haghighi ◽  
Jacob Kresslein ◽  
Duane Detwiler ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Methods ◽  
Manufacturing Processes ◽  
Complex Geometries ◽  
Manufacturing Constraints ◽  
Post Processing ◽  
Optimization Framework ◽  
Manufacturing Methods ◽  
Traditional Manufacturing

Topology optimization provides optimized solutions with complex geometries which are often not suitable for direct manufacturing without further steps or post-processing by the designer. There has been a recent progression towards linking topology optimization with additive manufacturing, which is less restrictive than traditional manufacturing methods, but the technology is still in its infancy being costly, time-consuming, and energy inefficient. For applications in automotive or aerospace industries, the traditional manufacturing processes are still preferred and utilized to a far greater extent. Adding manufacturing constraints within the topology optimization framework eliminates the additional design steps of interpreting the topology optimization result and converting it to viable manufacturable parts. Furthermore, unintended but inevitable deviations that occur during manual conversion from the topology optimized result can be avoided. In this paper, we review recent advances to integrate (traditional) manufacturing constraints in the topology optimization process. The focus is on the methods that can create manufacturable and well-defined geometries. The survey will discuss the advantages, limitations, and related challenges of manufacturability in topology optimization.

scholarly journals Gradient-based topology optimization of soft dielectrics as tunable phononic crystals

2021 ◽  
Author(s):  
Atul Kumar Sharma ◽  
Gal Shmuel ◽  
Oded Amir
Keyword(s):  
Topology Optimization ◽  
Electric Fields ◽  
Computational Cost ◽  
Optimization Method ◽  
Phononic Crystals ◽  
Frequency Bands ◽  
Design Variables ◽  
Gradient Based ◽  
Soft Dielectrics ◽  
Design Freedom

Dielectric elastomers are active materials that undergo large deformations and change their instantaneous moduli when they are actuated by electric fields. By virtue of these features, composites made of soft dielectrics can filter waves across frequency bands that are electrostatically tunable. To date, to improve the performance of these adaptive phononic crystals, such as the width of these bands at the actuated state, metaheuristics-based topology optimization was used. However, the design freedom offered by this approach is limited because the number of function evaluations increases exponentially with the number of design variables. Here, we go beyond the limitations of this approach, by developing an efficient gradient-based topology optimization method. The numerical results of the method developed here demonstrate prohibited frequency bands that are indeed wider than those obtained from the previous metaheuristics-based method, while the computational cost to identify them is reduced by orders of magnitude.

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.

scholarly journals Anisotropic Multicomponent Topology Optimization for Additive Manufacturing With Build Orientation Design and Stress-Constrained Interfaces

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Maximum Stress ◽  
Optimization Method ◽  
Stress Constraints ◽  
Material Behavior ◽  
Simultaneous Optimization ◽  
Structural Topology ◽  
Build Orientation ◽  
Interface Location ◽  
Component Interface

Abstract This paper presents a multicomponent topology optimization method for designing structures assembled from additively manufactured components, considering anisotropic material behavior for each component due to its build orientation, distinct material behavior, and stress constraints at component interfaces (i.e., joints). Based upon the multicomponent topology optimization (MTO) framework, the simultaneous optimization of structural topology, its partitioning, and the build orientations of each component is achieved, which maximizes an assembly-level structural stiffness performance subject to maximum stress constraints at component interfaces. The build orientations of each component are modeled by its orientation tensor that avoids numerical instability experienced by the conventional angular representation. A new joint model is introduced at component interfaces, which enables the identification of the interface location, the specification of a distinct material tensor, and imposing maximum stress constraints during optimization. Both 2D and 3D numerical examples are presented to illustrate the effect of the build orientation anisotropy and the component interface behavior on the resulting multicomponent assemblies.

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