SEMDOT: Smooth-edged material distribution for optimizing topology algorithm

Fu, Yun-Fei; Rolfe, Bernard; Chiu, Louis~N N.S.; Wang, Yanan; Huang, Xiaodong; Ghabraie, Kazem

doi:10.1016/j.advengsoft.2020.102921

Cited by 37 publications

(52 citation statements)

References 69 publications

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“…This study uses SEMDOT as an optimization platform to form smooth topologies and Langelaar's AM filter to obtain 3D self-supporting designs. SEMDOT is a new topology optimization platform that was developed by the authors [16], and its effectiveness has been thoroughly validated. For more details on the advantages of SEMDOT compared to some existing algorithms that are capable of generating smooth boundaries, see [15,16].…”

Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

“…SEMDOT is a new topology optimization platform that was developed by the authors [16], and its effectiveness has been thoroughly validated. For more details on the advantages of SEMDOT compared to some existing algorithms that are capable of generating smooth boundaries, see [15,16]. Langelaar's AM filter is a well-established restriction method that can be used with gradient-based topology optimization algorithms, and it has become one of the most extensively used methods for generating self-supporting designs in this community.…”

Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

“…where δ e is the deviation of the eth element before and after the update. A detailed discussion of this relationship is presented in [16].…”

Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

“…Given the discussions of the relationship betweenX e andX new e in [16], it can be assumed that ∂ξ new /∂ξ = 1 in this paper for simplicity. The sensitivity of C p (ξ) with respect toX can be calculated based on the derivation of adjoint sensitivity analysis as [5]:…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…As traditional element-based algorithms such as solid isotropic material with penalization (SIMP), rational material with penalization (RAMP) and bi-directional evolutionary structural optimization (BESO) will inevitably form non-smooth boundaries, post-processing or redesign methods have to be used to obtain accurate boundary information for the purpose of engineering applications [7][8][9], meaning that extra efforts have to be made after topology optimization. Given the significance of accurate boundary representation, some element-based algorithms that are capable of forming smooth boundaries such as multiresolution topology optimization (MTO) methods [10][11][12], elemental volume fraction-based methods [13][14][15][16] and a method using floating projection [17,18] have been developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT

Ghabraie

Rolfe

et al. 2020

Applied Sciences

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The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar’s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm—a new element-based topology optimization method capable of forming smooth boundaries—to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem—the force inverter design—is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar’s AM filter.

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Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

“…where δ e is the deviation of the eth element before and after the update. A detailed discussion of this relationship is presented in [16].…”

Section: Integrating Langelaar's Am Filter Into Semdot 21 Problem Statementmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT

Ghabraie

Rolfe

et al. 2020

Applied Sciences

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Framework to Improve the Energy Performance During Design for Additive Manufacturing

Yi¹,

Wu²,

Glatt³

et al. 2023

Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes

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Additive manufacturing (AM) is suitable for designing and producing complex components that are difficult or impossible to manufacture with conventional manufacturing processes. To ensure the design benefits of AM, novel design approaches such as structural topology optimization and cellular structure design are widely used and bring up the research domain of design for AM (DfAM). However, conventional DfAM approaches mainly focus on the geometry and manufacturability of AM components and rarely consider energy performance as an improvement objective. Given that the energy consumption in AM processes can be a great contributor to the overall environmental impact in the production stage with AM, the evaluation and improvement of the energy performance of AM should be considered in the DfAM approaches; otherwise, opportunities to improve the energy performance by changing product features are missed. To address this research question, we are proposing a framework that enables the evaluation and improvement of the energy performance of AM in the design stage resulting in a new method for DfAM. To validate the framework, two use cases are presented to illustrate the feasibility of developed methods and tools.

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A comprehensive review of educational articles on structural and multidisciplinary optimization

Wang

Zhao

Zhou

et al. 2021

Struct Multidisc Optim

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Ever since the publication of the 99-line topology optimization MATLAB code (top99) by Sigmund in 2001, educational articles have emerged as a popular category of contributions within the structural and multidisciplinary optimization (SMO) community. The number of educational papers in the field of SMO has been growing rapidly in recent years. Some educational contributions have made a tremendous impact on both research and education. For example, top99 (Sigmund in Struct Multidisc Optim 21(2):120-127, 2001) has been downloaded over 13,000 times and cited over 2000 times in Google Scholar. In this paper, we attempt to provide a systematic and comprehensive review of educational articles and codes in SMO, including topology, sizing, and shape optimization and building blocks. We first assess the papers according to the adopted methods, which include density-based, level-set, ground structure, and more. We then provide comparisons and evaluations on the codes from several key aspects, including techniques, efficiency, usability, readability, environment, and compatibility. In addition, we conduct numerical experiments on the reviewed codes using the benchmark cantilever beam example to provide feedback on the overall user experience. With a systematic review and comparison, this paper aims to offer insights on the educational values and practicality for employing these codes. We try to provide not only guidance for beginners to approach various optimization methods, but also a dictionary to direct readers to effectively target the relevant codes and building blocks based on their demands. Finally, based on the findings in this review paper, we provide some perspectives and recommendations for future educational contributions.

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SEMDOT: Smooth-edged material distribution for optimizing topology algorithm

Cited by 37 publications

References 69 publications

Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT

Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT

Framework to Improve the Energy Performance During Design for Additive Manufacturing

A comprehensive review of educational articles on structural and multidisciplinary optimization

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