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 Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

Application of Dynamic Space Reduction Method Based on Partial Correlation Analysis in Hull Optimization

2020 ◽  
pp. 1-12
Author(s):  
Qiang Zheng ◽  
Hai-Chao Chang ◽  
Zu-Yuan Liu ◽  
Bai-Wei Feng
Keyword(s):  
Rough Set ◽  
Optimization Design ◽  
Partial Correlation ◽  
Design Space ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Space Reduction ◽  
Design Variables ◽  
Hull Optimization ◽  
Optimization Efficiency

Hull optimization design based on computational fluid dynamics (CFD) is a highly computationally intensive complex engineering problem. Because of reasons such as many variables, spatially complex design performance, and huge computational workload, hull optimization efficiency is low. To improve the efficiency of hull optimization, a dynamic space reduction method based on a partial correlation analysis is proposed in this study. The proposed method dynamically uses hull-form optimization data to analyze and reduce the range of values for relevant design variables and, thus, considerably improves the optimization efficiency. This method is used to optimize the wave-making resistance of an S60 hull, and its feasibility is verified through comparison. 1. Introduction In recent years, to promote the rapid development of green ships, hull optimization methods based on computational fluid dynamics (CFD) have been widely used by many researchers, such as Tahara et al. (2011), Peri and Diez (2013), Kim and Yang (2010), Yang and Huang (2016), Chang et al. (2012), and Feng et al. (2009). However, hull optimization design is a typically complex engineering problem. It requires many numerical simulation calculations, and the design performance space is complex, which has resulted in low optimization efficiency and difficulty in obtaining a global optimal solution. Commonly used solutions include 1) efficient optimization algorithms, 2) approximate model techniques, and 3) high-performance cluster computers. However, these methods still cannot satisfy the engineering application requirements in terms of efficiency and quality of the solution. To solve the problem of low optimization efficiency and difficulty in obtaining an optimal solution in engineering optimization problems, many scholars have conducted research on design space reduction technology. Reungsinkonkarn and Apirukvorapinit (2014) applied the search space reduction (SSR) algorithm to the particle swarm optimization (PSO) algorithm, eliminating areas in which optimal solutions may not be found through SSR to improve the optimization efficiency of the algorithm. Chen et al. (2015) and Diez et al. (2014, 2015) used the Karhunen–Loeve expansion to evaluate the hull, eliminating the less influential factors to achieve space reduction modeling with fewer design variables. Further extensions to nonlinear dimensionality reduction methods can be found in D'Agostino et al. (2017) and Serani et al. (2019). Jeong et al. (2005) applied space reduction techniques to the aerodynamic shape optimization of the vane wheel, using the rough set theory and decision trees to extract aerofoil design rules to improve each target. Gao et al. (2009) and Wang et al. (2014) solved the problem of low optimization efficiency in the aerodynamic shape optimization design of an aircraft, by using analysis results of partial correlation, which reduced the range of values of relevant design variables to reconstruct the optimized design space. Li et al. (2013) divided the design space into several smaller cluster spaces using the clustering method, which is a global optimization method based on an approximation model, thus achieving design space reduction. Chu (2010) combined the rough set theory and the clustering method for application to the concept design stage of bulk carriers, thus realizing the exploration and reduction of design space. Feng et al. (2015) applied the rough set theory and the sequential space reduction method to the resistance optimization of typical ship hulls to achieve the reduction of design space. Wu et al. (2016) used partial correlation analysis to reduce the design space of variables of a KCS container ship to improve optimization efficiency. Most of the above space reduction methods need to sample and calculate the original design space in the early stage of optimization and then obtain the reduced design space through data mining. This process increases the computational cost of sampling, making it difficult to control optimization efficiency.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2017 ◽  
Vol 6 (2) ◽  
pp. 18-37 ◽  
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Complex Plane ◽  
Search Space ◽  
Regions Of Interest ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

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