Layout optimization method for magnetic circuit using multistep utilization of genetic algorithm combined with design space reduction

2012 ◽  
Vol 181 (3) ◽  
pp. 19-30
Author(s):  
Yoshifumi Okamoto ◽  
Yusuke Tominaga ◽  
Shuji Sato
Keyword(s):  
Genetic Algorithm ◽  
Design Space ◽  
Magnetic Circuit ◽  
Optimization Method ◽  
Layout Optimization ◽  
Space Reduction

Process Yield Improvement Through Optimum Design of Fixture Layouts in 3D Multistation Assembly Systems

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
T. Phoomboplab ◽  
D. Ceglarek
Keyword(s):  
Genetic Algorithm ◽  
Design Space ◽  
Assembly System ◽  
Industrial Case Study ◽  
Process Yield ◽  
Space Reduction ◽  
Complex Design ◽  
Hammersley Sequence ◽  
Multistation Assembly ◽  
Fixture System

Fixtures control the positions and orientations of parts in an assembly process. Inaccuracies of fixture locators or nonoptimal fixture layouts can result in the deviation of a workpiece from its design nominal and lead to overall product dimensional variability and low process yield. Major challenges involving the design of a set of fixture layouts for multistation assembly system can be enumerated into three categories: (1) high-dimensional design space since a large number of locators are involved in the multistation system, (2) large and complex design space for each locator since the design space represents the area of a particular part or subassembly surfaces on which a locator is placed, (here, the design space varies with a particular part design and is further expanded when parts are assembled into subassemblies), and (3) the nonlinear relations between locator nominal positions and key product characteristics. This paper presents a new approach to improve process yield by determining an optimum set of fixture layouts for a given multistation assembly system, which can satisfy (1) the part and subassembly locating stability in each fixture layout and (2) the fixture system robustness against environmental noises in order to minimize product dimensional variability. The proposed methodology is based on a two-step optimization which involves the integration of genetic algorithm and Hammersley sequence sampling. First, genetic algorithm is used for design space reduction by estimating the areas of optimal fixture locations in initial design spaces. Then, Hammersley sequence sampling uniformly samples the candidate sets of fixture layouts from those predetermined areas for the optimum. The process yield and part instability index are design objectives in evaluating candidate sets of fixture layouts. An industrial case study illustrates and validates the proposed methodology.

Process Yield Improvement Through Optimum Design of Fixture Layouts in 3D Multi-Station Assembly Systems

2007 ◽  
Author(s):  
T. Phoomboplab ◽  
D. Ceglarek
Keyword(s):  
Genetic Algorithm ◽  
Design Space ◽  
Industrial Case Study ◽  
Process Yield ◽  
Space Reduction ◽  
Multi Stage ◽  
Complex Design ◽  
Hammersley Sequence ◽  
System Robustness ◽  
Fixture System

This paper presents a new approach to improve process yield by determining an optimum set of fixture layouts for a given multi-station assembly system which can satisfy: (i) parts and subassemblies locating stability in each fixture layout; and (ii) fixture system robustness against environmental noises in order to minimize product dimensional variability. Three major challenges of the multi-stage assembly processes are addressed: (i) high-dimensional design space; (ii) large and complex design space of each locator; and (iii) the nonlinear relations between locator positions, also called Key Control Characteristics, and Key Product Characteristics. The proposed methodology conducts two-step optimization based on the integration of Genetic Algorithm and Hammersley Sequence Sampling. First, Genetic Algorithm is used for design space reduction by determining the areas of optimal fixture locations in initial design spaces. Then, Hammersley Sequence Sampling uniformly samples the candidate sets of fixture layouts from the areas predetermined by GA for the optimum. The process yield and part instability index are design objectives in evaluating candidate sets of fixture layouts. An industrial case study illustrates and validates the proposed methodology.

scholarly journals Urban Layout Optimization Based on Genetic Algorithm for Microclimate Performance in the Cold Region of China

2019 ◽  
Vol 9 (22) ◽  
pp. 4747
Author(s):  
Xiaodong Xu ◽  
Yupeng Liu ◽  
Wei Wang ◽  
Ning Xu ◽  
Ke Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Layout Optimization ◽  
Comfort Level ◽  
Block Type ◽  
Cold Region ◽  
Comfort Index ◽  
Street Level ◽  
The North ◽  
Block Level

A performance-driven sustainable urban design is an important step in adapting to local climates and improving outdoor comfort level. This study proposed investigating an urban layout optimization method, validated in an urban area in Shenyang City, a cold region, that comprises nine urban blocks. This study selected an urban block type and street width as optimization input indices and defined the percentage of acceptable universal thermal comfort index (UTCI) range as the objective. The genetic algorithm is applied to optimize and generate an urban block to maximize the percentage of acceptable UTCI range at urban block level and street level. When the comfort level is set as −17–20 °C, optimization can achieve 87.7% of acceptable UTCI in the urban block level and 90.3% at urban street level. To attain an urban layout with higher than 85% acceptable UTCI range, results found, in a cold region, multistory blocks are more suitable than high-rise blocks and open spaces; it would be better to place multistory blocks with high building enclosures on the north side to block cold wind.

scholarly journals An approximate high-dimensional optimization method using hierarchical design space reduction strategy

2021 ◽  
Vol 39 (2) ◽  
pp. 292-301
Author(s):  
Pengcheng Ye ◽  
Congcong Wang ◽  
Guang Pan
Keyword(s):  
Design Space ◽  
Optimization Method ◽  
Surrogate Models ◽  
Black Box ◽  
High Dimensional ◽  
Hierarchical Design ◽  
Reduction Strategy ◽  
Space Reduction ◽  
Ensemble Of Surrogates ◽  
Dimensional Optimization

To overcome the complicated engineering model and huge computational cost, a hierarchical design space reduction strategy based approximate high-dimensional optimization(HSRAHO) method is proposed to deal with the high-dimensional expensive black-box problems. Three classical surrogate models including polynomial response surfaces, radial basis functions and Kriging are selected as the component surrogate models. The ensemble of surrogates is constructed using the optimized weight factors selection method based on the prediction sum of squares and employed to replace the real high-dimensional black-box models. The hierarchical design space reduction strategy is used to identify the design subspaces according to the known information. And, the new promising sample points are generated in the design subspaces. Thus, the prediction accuracy of ensemble of surrogates in these interesting sub-regions can be gradually improved until the optimization convergence. Testing using several benchmark optimization functions and an airfoil design optimization problem, the newly proposed approximate high-dimensional optimization method HSRAHO shows improved capability in high-dimensional optimization efficiency and identifying the global optimum.

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