Research Papers

Active Contours With Stochastic Fronts and Mechanical Topology Optimization

[+] Author and Article Information
Alireza Kasaiezadeh

Research Associate
e-mail: sakasaie@uwaterloo.ca

Amir Khajepour

Canada Research Chair
e-mail: a.khajepour@uwaterloo.ca
Mechanical and Mechatronic Department,
University of Waterloo,
Ontario, N2L3G1Canada

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 18, 2012; final manuscript received February 19, 2013; published online March 28, 2013. Assoc. Editor: Shinji Nishiwaki.

J. Mech. Des 135(4), 041008 (Mar 28, 2013) (12 pages) Paper No: MD-12-1065; doi: 10.1115/1.4023869 History: Received January 18, 2012; Revised February 19, 2013

Active contours with stochastic fronts (ACSF) is developed and investigated in this article to address the dependency of the existing algorithms of topology optimization on the initial guesses. The promising results of ACSF confirms that the use of this approach leads to higher chance of escaping from local solutions compared to the classic level set method. ACSF as a special case of the stochastic active contours (SAC), has a simplified structure that makes its implementation easier, and at the same time it has a rigorous mathematical proof of convergence. Although propitious, there is still a slight chance of trapping scenarios for ACSF that is observed in the presented results.

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Grahic Jump Location
Fig. 1

A general load case including external and body forces

Grahic Jump Location
Fig. 2

Deflections at input and output ports due to the external force

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Fig. 5

Boundary conditions of case study 1

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Fig. 6

Boundary conditions of case study 2

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Fig. 8

Convergence pattern of ACSF for case study 1

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Fig. 9

Convergence pattern of ACSF for case study 2

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Fig. 3

Boundary condition of the Bridge problem

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Fig. 4

Convergence of the ACSF algorithm with annealing scheme for the Bridge problem shown in Table 3

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Fig. 7

Boundary conditions of case study 3

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Fig. 10

Convergence pattern of ACSF for case study 3




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