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International Conference on Advances in Materials and Manufacturing Processes

作者： An, Zhiguo Zhang, Yu Chongqing Jiaotong Univ Coll Mech Elect & Automobile Engn Chongqing 400074 Peoples R China

ISBN: (纸本)9780878492046

In sheet metal forming process, the input process parameters scatter and considerably result in unreliablity in practical production. Optimization for sheet metal forming process is often considered as a multi-objective problem. An optimizition strategy for high strength steel (HSS) sheet metal forming process was suggested based on response surface methodology (RSM). Latin Hypercube Sampling (LHS) method was introduced to design the rational experimental samples;the objective function was defined based on cracking factor wrinkle factor and severe thinning factor;the accurate response surface for sheet metal forming problem was built by Least Square Method;multi-objective genetic algorithm(MOGA) was adoped in optimization and Pareto solution was selected. The strategy was applied to analyze a HSS auto-part, the result has proved this method suitable for optimization design of HSS sheet metal forming process.

关键词： Response Surface Methodology Design of Experiment Sheet Metal Forming Latin Hypercube Sampling multi-objective genetic algorithm

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IPM motor rotor design by means of FEA-based multi-objective optimization

IPM motor rotor design by means of FEA-based multi-objective...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Pellegrino, G. Cupertino, F. Politecn Torino Cso Duca degli Abruzzi 24 I-10129 Turin Italy Politecn Bari I-70125 Bari Italy

ISBN: (纸本)9781424463916

The design optimization of IPM motors for wide speed ranges is pursued by means of a FEA-based multi-objective genetic algorithm (MOGA). Respect to previous works in the literature, the proposed approach evaluates the motor performance with a very limited number of simulations, making FEA optimization more attractive. The 3 goal functions (motor torque, torque ripple and flux weakening capability) are evaluated by means of 7 static FEA runs, that means nearly 20 seconds per tentative motor with a laptop computer. The paper is focused on the rotor design, since it is the most controversial part of IPM design and the most difficult to be modeled due to magnetic saturation. Three different approaches are presented: a fast one, based on 2-objective optimization, a hybrid one, based on 2-objective optimization and 3-objective refinement, and actual 3-objective optimization. The results presented here will be the base for future, more comprehensive optimization.

关键词： FEA IPM motor rotor design finite element analysis flux weakening capability genetic algorithms magnetic saturation motor torque multi-objective genetic algorithm permanent magnet motors rotors torque ripple wide speed ranges

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Configuration Analysis and Optimization of Collaborative Robots 3

Configuration Analysis and Optimization of Collaborative Rob...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Tian, Yong Wang, Hongguang Pan, Xinan Hu, Mingwei Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538662434

The configuration of collaborative robots is analyzed and optimized based on the Kriging model. First, through analyzing configurations of the existing collaborative robots, it can be seen that the offset is an important factor affecting the configuration, which is divided into S type and V type by the influence on the range of motion of the joint. Then, based on the Kriging model, the multi-objective optimization function is established, which takes the length and offset of each link as parameter variables and regards the global performance index, the workspace volume index and the workspace dexterity performance index as optimization objective. And the influence of the link and offset parameters of the robot on the robot performance index is analyzed. In the end, the pareto optimal solution set and the optimization configurations are obtained using multi-objective genetic algorithm, and the performance is improved, the solution provides theoretical basis for the configuration design of collaborative robots.

关键词： collaborative robots model offset multi-objective genetic algorithm optimization

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Cell formation using modular-reconfigurable machines

Cell formation using modular-reconfigurable machines

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16th International Symposium of the Danube-Adria-Association-for-Automation-and-Manufacturing

作者： Pattanaik, L. N. Jain, P. K. Mehta, N. K.

ISBN: (纸本)9783901509469

In this research, an approach is made to design machine cells using modular-reconfigurable machines to achieve certain characteristics of reconfigurable manufacturing. Each machine considered in the model consists of some basic and auxiliary machine modules. By changing the auxiliary modules, different operations can be performed on the machines. A similarity measure among machines based on production flow information and auxiliary module requirement is developed. Machine cells are identified using a multi-objective evolutionary genetic algorithm for a set of parts with parameters like volumes of production, alternative operation based process plans etc. The two objectives considered are minimization of inter-cell movement and total changes in auxiliary modules for the given production horizon. An illustrative problem and experimental results are given.

关键词： cell formation modular-reconfigurable machines multi-objective genetic algorithm

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Surrogate-Based multi-objective Optimization and Data Mining of Vortex Generators on a Transonic Infinite-Wing

Surrogate-Based Multi-Objective Optimization and Data Mining...

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IEEE Congress on Evolutionary Computation

作者： Namura, Nobuo Obayashi, Shigeru Jeong, Shinkyu Tohoku Univ Inst Fluid Sci Sendai Miyagi 9808577 Japan Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9781479904549;9781479904532

multi-objective optimization and data mining of vortex generators (VGs) on a transonic infinite-wing was performed using computational fluid dynamics (CFD), surrogate models, and a multi-objective genetic algorithm (MOGA). VGs arrangements were defined by five design variables: height, length, incidence angle, spacing, and chord location. The objective functions which should be maximized were three: lift-drag ratio at low angle of attack, lift coefficient at high angle of attack, and chordwise separation location at high angle of attack. In order to evaluate these objective functions of each individual in MOGA, the response surface methodology with Kriging model and the modified version of it was employed because CFD analysis of the wing with VG requires a large computational time. Two types of data mining method: analysis of variance (ANOVA) and self-organizing map (SOM), were applied to the result of the optimization. It was revealed by ANOVA that the ratio of spacing to height and the incidence angle had significant influences to maximizing each objective function. By using SOM, VG designs were split into four types which have different aerodynamic characteristics respectively. The appropriate values of parameters were identified by SOM.

关键词： multi-objective genetic algorithm Kriging model radial basis function networks self-organizing map analysis of variance computational fluid dynamics

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Design for product-embedded disassembly pathways

Design for product-embedded disassembly pathways

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IEEE International Conference on Automation Science and Engineering

作者： Takeuchi, S Saitou, K Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

ISBN: (纸本)0780394259

This paper presents a computational method for designing an assembly with multiple built-in disassembly pathways, each of which can be activated to retrieve certain components. It is motivated by the global sales of consumer products whose optimal end-of-life options vary geographically due to local recycling/reuse infrastructures and regulatory requirements. Given the sets of components to be retrieved at each location, the method simultaneously determines the spatial configurations of components and locator features, such that each set of desired components is retrieved via a domino-like "self-disassembly" process triggered by the removal of a fastener. A multi-objective generic algorithm is utilized to search for Pareto-optimal designs in terms of the realization of the desired disassembly pathways, the satisfaction of distance specifications among components, the minimization of disassembly cost at each location, and the efficient use of oncomponent locator features. A case study demonstrates the feasibility of the method.

关键词： design for disassembly computer aided design product design automation multi-objective genetic algorithm

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multi-item Supplier Selection Model with Fuzzy Risk Analysis Studied by Possibility and Necessity Constraints

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FUZZY INFORMATION AND ENGINEERING 2015年第4期7卷 451-474页

作者： Patra, Kartik Mondal, Shyamal Kumar Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 WB India

Three different supplier selection models have been developed in crisp and fuzzy environments. Here two objective functions have been considered, profit and risk. In this paper, profit has been maximized and risk has been minimized with some constraints. Each supplier has an limited capacity. The purchasing cost of each item from different supplier as well as associative risk is known. The total space and budget of a retailer are constant. In Model I, all the parameters are considered as crisp. In Model II, the demand has been considered as fuzzy. In Model III, the risk values and demand have been considered as fuzzy. To defuzzyfy the fuzzy constraints, necessity and possibility have been introduced. To defuzzyfy the fuzzy objective, two different methods, credibility measure and alpha-cut method have been introduced. All the models have been illustrated numerically using multi-objective genetic algorithm (MOGA). Also a sensitivity analysis has been done taking different sets of risk values and a comparison result has been shown for credibility measure and alpha-cut-method-for Model-III.

关键词： Supplier selection Necessity Possibility Risk multi-objective genetic algorithm

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Optimal and Feasible Cloud Resource Configurations Generation Method for Genomic Analytics Applications 10

Optimal and Feasible Cloud Resource Configurations Generatio...

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10th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Miura, Katsunori Powell, Courtney Munetomo, Masaharu Otaru Univ Otaru Hokkaido 0478501 Japan Hokkaido Univ Sapporo Hokkaido 0600811 Japan

ISBN: (纸本)9781538678992

This paper proposes a new method that efficiently generates optimal and feasible cloud resource configurations for deploying genomic analytics applications. The proposed method generates optimal and feasible configurations with respect to system requirements by employing two different problem-solving techniques: an equivalent transformation algorithm and a multiobjective genetic algorithm. The equivalent transformation algorithm first generates feasible configurations through computation based on (1) state replacement of clause sets representing various resource configurations and (2) evaluation of their equivalence in terms of declarative meaning to the given requirements. Subsequently, the multi-objective genetic algorithm identifies the optimal configurations with respect to estimated financial cost and computational performance. The input to the proposed method is a logical formula describing the system requirements, whereas the output is a set of unit clauses representing Paretooptimal and feasible cloud resource configurations for deploying a given genomic analytics application. The results of experiments conducted using a sample genomic analytics workflow and Amazon EC2 instances verify the efficacy of the proposed method.

关键词： Cloud resource selection Predicate logic Equivalent transformation algorithm multi-objective genetic algorithm Genomic analytics application

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Improving an Evolutionary multi-objective algorithm for the Biclustering of Gene Expression Data

Improving an Evolutionary Multi-objective Algorithm for the ...

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IEEE Congress on Evolutionary Computation

作者： Brizuela, Carlos A. Luna-Taylor, Jorge E. Martinez-Perez, Israel Guillen, Hugo A. Rodriguez, David O. Beltran-Verdugo, Armando CICESE Dept Comp Sci Ensenada Baja California Mexico ITLP Dept Syst & Computat La Paz Mexico

ISBN: (纸本)9781479904549;9781479904532

The development of new technologies for the design of DNA microarrays has boosted the generation of large volumes of biological data, which requires the development of efficient computational methods for their analysis and annotation. Among these methods, biclusters generation algorithms attempt to identify coherent associations of genes and experimental conditions. In this paper, we introduce an improved version of a multi-objective genetic algorithm to find large biclusters that are, at the same time, highly homogeneous. The proposed improvement uses a group based representation for the genes-conditions associations rather than long binary strings. To assess the proposal performance the algorithm is applied to generate biclusters for two real gene expression data: Saccharomyces Cerevisiae with 2884 genes and 17 conditions, and the human B cells Lymphoma with 4026 genes and 96 conditions. The results of computational experiments show that the proposed approach outperforms current state-of-the-art algorithms on these data sets.

关键词： biclustering gene expression multi-objective genetic algorithm group based representation microarray DNA

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A New Hybrid Optimization of Reactive Optimization and Voltage Stability

A New Hybrid Optimization of Reactive Optimization and Volta...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhou Ling Hao Fuzhong Zhao Feng Tan Jie Wang Dongping Wang Aiqin Hou Lifeng HoHai Univ Sch Elect Engn Nanjing Jiangsu Peoples R China Jiyuan Power Supply Bur Jiyuan Peoples R China

ISBN: (纸本)9781424448135

improving voltage quality and reducing power loss were the main goals of power system reactive optimization. The traditional method could reach the best economic benefit by optimizing the power flow, when the margin of the power system was enough. Nowadays, the safety grows more and more important because of the development of power system. Static voltage stability margin is an important index of power system safety. A new hybrid optimization had been put forward in this paper. The goal of this method was giving consideration to reducing power loss and increasing static voltage stability margin. At last, a simulation of IEEE-30 was made in this paper. The result of simulation showed this method is very effective.

关键词： voltage stability reactive optimization multi-objective genetic algorithm

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