scholarly journals Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing

2021 ◽  
Vol 11 (11) ◽  
pp. 5257
Author(s):  
Hossein R. Najafabadi ◽  
Tiago G. Goto ◽  
Mizael S. Falheiro ◽  
Thiago C. Martins ◽  
Ahmad Barari ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Topology Optimization ◽  
Computer Aided Design ◽  
Digital Design ◽  
Density Filter ◽  
Gradient Based ◽  
History Of ◽  
Adaptive Neighborhood ◽  
Gray Areas ◽  
Structure Of Knowledge

Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories of gradient-based and non-gradient-based methods. In recent years, significant attention has been brought to the non-gradient-based methods, mainly because they do not demand access to the derivatives of the objective functions. This property makes them well compatible to the structure of knowledge in the digital design and simulation domains, particularly in Computer Aided Design and Engineering (CAD/CAE) environments. These methods allow for the generation and evaluation of new evolutionary solutions without using the sensitivity information. In this work, a new non-gradient TO methodology using a variation of Simulated Annealing (SA) is presented. This methodology adaptively adjusts newly-generated candidates based on the history of the current solutions and uses the crystallization heuristic to smartly control the convergence of the TO problem. If the changes in the previous solutions of an element and its neighborhood improve the results, the crystallization factor increases the changes in the newly random generated solutions. Otherwise, it decreases the value of changes in the recently generated solutions. This methodology wisely improves the random exploration and convergence of the solutions in TO. In order to study the role of the various parameters in the algorithm, a variety of experiments are conducted and results are analyzed. In multiple case studies, it is shown that the final results are well comparable to the results obtained from the classic gradient-based methods. As an additional feature, a density filter is added to the algorithm to remove discontinuities and gray areas in the final solution resulting in robust outcomes in adjustable resolutions.

scholarly journals Post-Processing of non gradient-based Topology Optimization with Simulated Annealing

2021 ◽  
Vol 54 (1) ◽  
pp. 755-760
Author(s):  
Hossein R. Najafabadi ◽  
Tiago Goto ◽  
Mizael Falheiro ◽  
Thiago C. Martins ◽  
Ahmad Barari ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Topology Optimization ◽  
Post Processing ◽  
Gradient Based

scholarly journals A Comprehensive Review of Isogeometric Topology Optimization: Methods, Applications and Prospects

2020 ◽  
Vol 33 (1) ◽  
Author(s):  
Jie Gao ◽  
Mi Xiao ◽  
Yan Zhang ◽  
Liang Gao
Keyword(s):  
Topology Optimization ◽  
Computer Aided Design ◽  
Numerical Technique ◽  
Optimization Methods ◽  
Negative Influence ◽  
Comprehensive Overview ◽  
Promising Alternative ◽  
Computer Aided ◽  
Optimal Material ◽  
Material Layout

AbstractTopology Optimization (TO) is a powerful numerical technique to determine the optimal material layout in a design domain, which has accepted considerable developments in recent years. The classic Finite Element Method (FEM) is applied to compute the unknown structural responses in TO. However, several numerical deficiencies of the FEM significantly influence the effectiveness and efficiency of TO. In order to eliminate the negative influence of the FEM on TO, IsoGeometric Analysis (IGA) has become a promising alternative due to its unique feature that the Computer-Aided Design (CAD) model and Computer-Aided Engineering (CAE) model can be unified into a same mathematical model. In the paper, the main intention is to provide a comprehensive overview for the developments of Isogeometric Topology Optimization (ITO) in methods and applications. Finally, some prospects for the developments of ITO in the future are also presented.

BIM based decision-support tool for automating design to fabrication process of freeform lattice space structure

2021 ◽  
pp. 095605992110338
Author(s):  
Saeid Haghir ◽  
Ramtin Haghnazar ◽  
Sara Saghafi Moghaddam ◽  
Danial Keramat ◽  
Mohammad Reza Matini ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Decision Support Tool ◽  
Computational Design ◽  
Space Structure ◽  
Digital Design ◽  
Information Modeling ◽  
Freeform Surfaces ◽  
Geometric Problem ◽  
Support Tool ◽  
Lattice Space

Complex freeform surfaces and structures are increasingly designed and used in the product and building industry due to the advances in mathematics and digital design tools. However, there is still a gap between designing freeform surfaces and fabricating them. The process of preparing freeform surfaces’ shop drawings is complicated, time-consuming, and lacks the mutual understanding among the stakeholders. Computational design and Building Information Modeling (BIM) can serve as a mediator agent for the integration of design goals with the geometric logic of constructability. They can also facilitate creating platforms for designing and evaluating freeform structures. This open-ended qualitative research attempts to develop a systematic methodology for automating the design and construction drafting process of freeform lattice space structure. Solving this complex geometric problem aims to benefit the design for construction and manufacturers and shrink the cost and time of the process. The study employs a 3D computer-aided design (CAD) tool and introduces an algorithm that generates a BIM model. The BIM model contains shop drawings and suggests the specifications of the main elements, such as beams, glass panels, and nodes.

High-Impact Practices in Materials Science Education: Student Research Internships Leading to Pedagogical Innovation in STEM Laboratory Learning Activities

MRS Advances ◽  
2017 ◽  
Vol 2 (31-32) ◽  
pp. 1667-1672 ◽  
Author(s):  
Lon A. Porter
Keyword(s):  
Computer Aided Design ◽  
Materials Science ◽  
Undergraduate Research ◽  
Science Research ◽  
High Impact ◽  
Digital Design ◽  
Education Student ◽  
Research Experience ◽  
Student Research ◽  
Pedagogical Innovation

ABSTRACTTraditional lecture-centered approaches alone are inadequate for preparing students for the challenges of creative problem solving in the STEM disciplines. As an alternative, learnercentered and other high-impact pedagogies are gaining prominence. The Wabash College 3D Printing and Fabrication Center (3D-PFC) supports several initiatives on campus, but one of the most successful is a computer-aided design (CAD) and fabrication-based undergraduate research internship program. The first cohort of four students participated in an eight-week program during the summer of 2015. A second group of the four students was successfully recruited to participate the following summer. This intensive materials science research experience challenged students to employ digital design and fabrication in the design, testing, and construction of inexpensive scientific instrumentation for use in introductory STEM courses at Wabash College. The student research interns ultimately produced a variety of successful new designs that could be produced for less than $25 per device and successfully detect analytes of interest down to concentrations in the parts per million (ppm) range. These student-produced instruments have enabled innovations in the way introductory instrumental analysis is taught on campus. Beyond summer work, the 3D-PFC staffed student interns during the academic year, where they collaborated on various cross-disciplinary projects with students and faculty from departments such as mathematics, physics, biology, rhetoric, history, classics, and English. Thus far, the student work has led to three campus presentations, four presentations at national professional conferences, and three peer-reviewed publications. The following report highlights initial progress as well as preliminary assessment findings.

Topology Optimization, Additive Layer Manufacturing, and Experimental Testing of an Air-Cooled Heat Sink

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Ercan M. Dede ◽  
Shailesh N. Joshi ◽  
Feng Zhou
Keyword(s):  
Topology Optimization ◽  
Heat Sink ◽  
Computer Aided Design ◽  
Experimental Testing ◽  
Jet Impingement ◽  
Coefficient Of Performance ◽  
Air Cooling ◽  
Additive Layer Manufacturing ◽  
Cloud Data ◽  
Layer Manufacturing

Topology optimization of an air-cooled heat sink considering heat conduction plus side-surface convection is presented. The optimization formulation is explained along with multiple design examples. A postprocessing procedure is described to synthesize manifold or “water-tight” solid model computer-aided design (CAD) geometry from three-dimensional (3D) point-cloud data extracted from the optimization result. Using this process, a heat sink is optimized for confined jet impingement air cooling. A prototype structure is fabricated out of AlSi12 using additive layer manufacturing (ALM). The heat transfer and fluid flow performance of the optimized heat sink are experimentally evaluated, and the results are compared with benchmark plate and pin-fin heat sink geometries that are conventionally machined out of aluminum and copper. In two separate test cases, the experimental results indicate that the optimized ALM heat sink design has a higher coefficient of performance (COP) relative to the benchmark heat sink designs.

An Improved Shape Annealing Method for Truss Topology Generation

1994 ◽  
Author(s):  
Giridhar Reddy ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Truss Structures ◽  
Model Design ◽  
Topology Generation ◽  
Truss Topology ◽  
Gradient Based ◽  
Annealing Algorithm ◽  
Annealing Method

Abstract A method for the design of truss structures which encourages lateral exploration, pushes away from violated spaces, models design intentions, and produces solutions with a wide variety of characteristics is introduced. An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, implements the method. The algorithm features a shape grammar to model design intentions, an ability to incorporate geometric constraints to avoid obstacles, and a shape optimization method using only simulated annealing with more consistent convergence characteristics; no traditional gradient-based techniques are employed. The improved algorithm is illustrated on various structural examples generating a variety of solutions based on a simple grammar.

Finding Better Local Optima in Topology Optimization via Tunneling

2018 ◽  
Author(s):  
Shanglong Zhang ◽  
Julián A. Norato
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Structural Response ◽  
Free Form ◽  
Local Optima ◽  
Design Representation ◽  
Tunneling Method ◽  
Optimization Parameters ◽  
Gradient Based ◽  
Geometric Primitives

Topology optimization problems are typically non-convex, and as such, multiple local minima exist. Depending on the initial design, the type of optimization algorithm and the optimization parameters, gradient-based optimizers converge to one of those minima. Unfortunately, these minima can be highly suboptimal, particularly when the structural response is very non-linear or when multiple constraints are present. This issue is more pronounced in the topology optimization of geometric primitives, because the design representation is more compact and restricted than in free-form topology optimization. In this paper, we investigate the use of tunneling in topology optimization to move from a poor local minimum to a better one. The tunneling method used in this work is a gradient-based deterministic method that finds a better minimum than the previous one in a sequential manner. We demonstrate this approach via numerical examples and show that the coupling of the tunneling method with topology optimization leads to better designs.

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.

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.

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