Combining Interactive Exploration and Optimization for Assembly Design

This paper presents an integrated framework for conceptual assembly design. Because the complexity of assembly design leads to extremely large design spaces, adequate support of design space exploration is a key issue that must be addressed. CAMF allows the designer to manage the overall design process and explore the design space through explicit representation of design stages and their relationships (history), assembly design constraints, and rationale. The designer is free to use both bottom-up or top-down approaches to explore different assembly configurations. Exploration of the design space is further enabled by incorporating a simulated annealing-based refinement tool that allows the designer to rapidly complete partial designs, refine complete designs, and generate multiple design alternatives.

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Combining Interactive Exploration and Optimization for Assembly Design

Volume 3: 22nd Design Automation Conference ◽

10.1115/96-detc/dac-1482 ◽

1996 ◽

Author(s):

Gerard Jounghyun Kim ◽

Simon Szykman

Keyword(s):

Simulated Annealing ◽

Design Process ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Design History ◽

Top Down ◽

Design Exploration ◽

Assembly Design ◽

Design Alternatives

Abstract This paper presents an integrated framework for assembly design. The framework allows the designer to represent knowledge about the design process and constraints, as well as information about the artifact being designed, design history and rationale. Because the complexity of assembly design leads to extremely large design spaces, adequately supporting design space exploration is a key issue that must be addressed. This is achieved in part by allowing the designer to use both top-down and bottom-up approaches to assembly design. Exploration of the design space is further enabled by incorporating a simulated annealing-based optimization tool that allows the designer to rapidly complete partial designs, refine complete designs, and generate multiple design alternatives.

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Computational Design Synthesis: A Model-Based Approach for Complex Systems

Volume 3: Advanced Composite Materials and Processing; Robotics; Information Management and PLM; Design Engineering ◽

10.1115/esda2012-82502 ◽

2012 ◽

Cited By ~ 1

Author(s):

Nicolas Albarello ◽

Jean-Baptiste Welcomme

Keyword(s):

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Design Rules ◽

System Architectures ◽

Architecture Model ◽

Model Based ◽

Design Alternatives ◽

Systems Architectures ◽

The Impact

The design of systems architectures often involve a combinatorial design-space made of technological and architectural choices. A complete or large exploration of this design space requires the use of a method to generate and evaluate design alternatives. This paper proposes an innovative approach for the design-space exploration of systems architectures. The SAMOA (System Architecture Model-based OptimizAtion) tool associated to the method is also introduced. The method permits to create a large number of various system architectures combining a set of possible components to address given system functions. The method relies on models that are used to represent the problem and the solutions and to evaluate architecture performances. An algorithm first synthesizes design alternatives (a physical architecture associated to a functional allocation) based on the functional architecture of the system, the system interfaces, a library of available components and user-defined design rules. Chains of components are sequentially added to an initially empty architecture until all functions are fulfilled. The design rules permit to guarantee the viability and validity of the chains of components and, consequently, of the generated architectures. The design space exploration is then performed in a smart way through the use of an evolutionary algorithm, the evolution mechanisms of which are specific to system architecting. Evaluation modules permit to assess the performances of alternatives based on the structure of the architecture model and the data embedded in the component models. These performances are used to select the best generated architectures considering constraints and quality metrics. This selection is based on the Pareto-dominance-based NSGA-II algorithm or, alternatively, on an interactive preference-based algorithm. Iterating over this evolution-evaluation-selection process permits to increase the quality of solutions and, thus, to highlight the regions of interest of the design-space which can be used as a base for further manual investigations. By using this method, the system designers have a larger confidence in the optimality of the adopted architecture than using a classical derivative approach as many more solutions are evaluated. Also, the method permits to quickly evaluate the trade-offs between the different considered criteria. Finally, the method can also be used to evaluate the impact of a technology on the system performances not only by a substituting a technology by another but also by adapting the architecture of the system.

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Design space exploration revisited

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060406060100 ◽

2006 ◽

Vol 20 (2) ◽

pp. 113-119 ◽

Cited By ~ 7

Author(s):

PIETER H.G. VAN LANGEN ◽

FRANCES M.T. BRAZIER

Keyword(s):

Design Process ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Explicit Representation ◽

Design Exploration ◽

Partial Design ◽

Additional Information ◽

Design Object ◽

Design Requirements

Design involves reasoning about descriptions of design artifacts, reasoning about design requirements, and reasoning about design process objectives (such as keeping to deadlines and available budget). Reasoning about these three aspects occurs during exploration, generation, and evaluation of partial design descriptions. Design space exploration involves exploration in all three related spaces: the space of partial descriptions of design artifacts, the space of design requirements, and the space of design process objectives. These spaces are vast. Explicit representation of the relations between elements in these three spaces provides the additional information needed to understand and reuse descriptions of partial design process traces, and to guide design exploration. In their Keynote Article, Woodbury and Burrow describe one of these spaces, namely, the space of design object descriptions, as a network of partial and intentional descriptions of design artifacts. The links between partial descriptions represent paths in design processes. Making the information compiled in these paths of exploration explicit, as proposed in this paper, extends the approach described by Woodbury and Burrow, increasing options for accessibility.

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Optimization of patch antennas via multithreaded simulated annealing based design exploration

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2017.06.004 ◽

2017 ◽

Vol 4 (4) ◽

pp. 249-255 ◽

Cited By ~ 2

Author(s):

James E. Richie ◽

Cristinel Ababei

Keyword(s):

Simulated Annealing ◽

Design Space Exploration ◽

Design Space ◽

Simulated Annealing Algorithm ◽

Space Exploration ◽

Software Framework ◽

Patch Antennas ◽

Microstrip Patch Antennas ◽

Speed Up ◽

Annealing Algorithm

Abstract In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreading to implement a manager-workers execution strategy. The number of worker threads is the same as the number of cores of the computer that is utilized. Thus, the computational runtime of the proposed software framework enables effective design space exploration. Simulations demonstrate the effectiveness of the proposed software framework. Highlights A software framework for the optimization of the design of microstrip patch antennas. A simulated annealing algorithm to perform the design space exploration. The popular MEEP simulator is employed to evaluate explored solutions for accuracy. Multithreading is used as a technique to speed-up the proposed tool.

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