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Article

Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing

1
Laboratory of Computational Geometry, Escola Politécnica da Universidade de São Paulo, São Paulo 05508-030, Brazil
2
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (Ontario Tech), Oshawa, ON 05508-030, Canada
3
Instituto de Ciências Agrária e Tecnológica, Universidade Federal de Rondonópolis, Rondonópolis 78736-900, Brazil
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Antonella Petrillo
Appl. Sci. 2021, 11(11), 5257; https://doi.org/10.3390/app11115257
Received: 30 April 2021 / Revised: 30 May 2021 / Accepted: 1 June 2021 / Published: 5 June 2021
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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. View Full-Text
Keywords: smart design and manufacturing; topology optimization; Simulated Annealing; crystallization factor; adaptive neighborhood; non-gradient to; density filter smart design and manufacturing; topology optimization; Simulated Annealing; crystallization factor; adaptive neighborhood; non-gradient to; density filter
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MDPI and ACS Style

R. Najafabadi, H.; G. Goto, T.; Falheiro, M.S.; C. Martins, T.; Barari, A.; S. G. Tsuzuki, M. Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing. Appl. Sci. 2021, 11, 5257. https://doi.org/10.3390/app11115257

AMA Style

R. Najafabadi H, G. Goto T, Falheiro MS, C. Martins T, Barari A, S. G. Tsuzuki M. Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing. Applied Sciences. 2021; 11(11):5257. https://doi.org/10.3390/app11115257

Chicago/Turabian Style

R. Najafabadi, Hossein, Tiago G. Goto, Mizael S. Falheiro, Thiago C. Martins, Ahmad Barari, and Marcos S. G. Tsuzuki. 2021. "Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing" Applied Sciences 11, no. 11: 5257. https://doi.org/10.3390/app11115257

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