Multi-Objective Optimization of a 2-DoF Purely Translational Parallel Manipulator

2011 ◽  
Vol 317-319 ◽  
pp. 794-798
Author(s):  
Zhi Bin Li ◽  
Yun Jiang Lou ◽  
Yong Sheng Zhang ◽  
Ze Xiang Li

The paper addresses the multi-objective optimization of a 2-DoF purely translational parallel manipulator. The kinematic analysis of the Proposed T2 parallel robot is introduced briefly. The objective functions are optimized simultaneously to improve Regular workspace Share (RWS) and Global Conditioning Index (GCI). A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (controlled ENSGA-II) is used to find the Pareto front. The optimization results show that this method is efficient. The parallel manipulator prototype is also exhibited here.

2013 ◽  
Vol 37 (2) ◽  
pp. 135-160 ◽  
Author(s):  
Sabbavarapu Ramana Babu ◽  
Vegesina Ramachandra Raju ◽  
Koona Ramji

This paper presents an optimal kinematic design for a general type of 3-RPS spatial parallel manipulator based on multi-objective optimization. The objective functions considered are Global Conditioning Index (GCI), Global stiffness Index (GSI) and Workspace volume. The objective functions are optimized simultaneously to improve the dexterity as well as the workspace volume which represents the working capacity of a parallel manipulator. A multi-objective Evolutionary Algorithm based on the control elitist non-dominated sorting genetic algorithm is adopted to find the true optimal Pareto front. A constraint Jacobian matrix is derived analytically and the manipulator workspace is generated by numerical search method. The static analysis of the manipulator is also carried out to determine the compliance of the end-effecter.


Author(s):  
Andrew J. Robison ◽  
Andrea Vacca

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.


Author(s):  
Renaud Henry ◽  
Damien Chablat ◽  
Mathieu Porez ◽  
Frédéric Boyer ◽  
Daniel Kanaan

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.


Author(s):  
Damien Chablat ◽  
Ste´phane Caro ◽  
Raza Ur-Rehman ◽  
Philippe Wenger

This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-PRR, 3-RPR and 3-RRR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.


2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.


2014 ◽  
Vol 974 ◽  
pp. 402-407 ◽  
Author(s):  
Akhtar Waseem ◽  
Jian Fei Su ◽  
Wu Yi Chen ◽  
Peng Fei Sun

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox ofMATLABis used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.


2016 ◽  
Vol 10 (1) ◽  
pp. 46-57 ◽  
Author(s):  
Shanyi Xie ◽  
Ruicong Zhai ◽  
Xianhu Liu ◽  
Baoguo Li ◽  
Kai Long ◽  
...  

Microgrid is one practical infrastructure to integrate Distributed Generations (DGs) and local loads. Its optimal operating strategy has aroused great attention in recent years. This paper mainly focuses on the multi-objective optimization of DGs in microgrid by using self-adaptive genetic algorithm (GA) and fuzzy decision. Five objective functions are taken into account comprising voltage offset, transmission loss, construction cost, purchase cost and the environmental cost. In the algorithm, self-adaptation in population size, mutation probability, selection and standardization of objective functions is developed to enhance the speed and efficiency of the algorithm. Moreover, fuzzy decision is applied to determine the final solution. Simulation results show this algorithm can effectively find the optimal solution and improve the real-time control of microgrid, which implies the possibility of potential applications in microgrid energy management system.


2021 ◽  
pp. 1-12
Author(s):  
Cis De Maesschalck ◽  
Valeria Andreoli ◽  
Guillermo Paniagua ◽  
Tyler Gillen ◽  
Brett Barker

Abstract The optimization of the turbine tip geometry remains vital to create more efficient and durable engines. Balancing the aerodynamic and thermal aspects, while maintaining mechanical integrity is key to reshape one of the most vulnerable parts of the entire engine. The increasing turbine gas temperatures, combined with the aerodynamically penalizing overtip leakage vortex, makes the design of the tip a truly multidisciplinary challenge. While many earlier efforts focused on uncooled geometries, or studied the aerothermal impact with a fixed cooling configuration, the current paper presents the outcome of a multi-objective optimization where both the squealer rim geometry and cooling injection pattern were allowed to vary simultaneously. This study seeks a further synergistic aerothermal benefit through combining a quasi-fully arbitrary cooling arrangement, with mutating squealer rim structures. Specifically, the current manuscript presents the results of over 330 cooled and uncooled squealer tip geometries. The turbine tip was automatically altered using a novel parametrization strategy, varying both the squealer rim structures as well as the size and location of the cooling holes. The aerodynamic and thermal characteristics of every design were evaluated through 3D CFD, adopting high-density hexahedral grids. A multi-objective differential evolution algorithm was used to obtain a Pareto front which maximizes the aerodynamic efficiency, while minimizing the overtip thermal loads. Eventually, a detailed investigation and robustness study was performed on a set of prime squealer geometries, to further investigate the aerodynamic flow topology, and the effect of various cooling injection schemes on the heat transfer patterns.


Author(s):  
Dan Zhang ◽  
Bin Wei

Differential evolution (DE) and Pareto front theory are used to optimize stiffness in three directions and workspace of a 3UPU manipulator. Stiffness of the mechanism in each direction is analyzed, and it comes to the fact when stiffness in x and y directions increase, stiffness in z direction will decrease and therefore, the sum of stiffness in x and y and stiffness in z are optimized simultaneously by applying DE, but those two objectives are not in the same scale, a normalization of objectives is therefore considered. Furthermore, workspace volume of the mechanism is analyzed and optimized by using DE. By comparing landscapes of stiffness and workspace volume, one finds stiffness in z has same trend with workspace volume whereas stiffness in x and y and workspace volume conflicts. By employing Pareto front theory and DE, the sum of stiffness in x and y and workspace volume are optimized simultaneously.


Author(s):  
Zhongran Chi ◽  
Haiqing Liu ◽  
Shusheng Zang

This paper discusses the approach of cooling design optimization of a HPT endwall with 3D Conjugate Heat Transfer (CHT) CFD applied. This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which were different for each cooling cavity. The optimization objectives were to reduce the wall temperature level and also to increase the aerodynamic performance of the gas turbine. The optimization methodology consisted of an in-house parametric design & CFD mesh generation tool, a CHT CFD solver, a database of wall temperature distributions, a metamodel, and a genetic algorithm (GA) for evolutionary multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently predict the aerodynamic loss and the wall temperature distribution of a new individual based on the database, was developed and coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to accelerate the optimization process. Through optimization search, the Pareto front of the problem was found costing only tens of CFD runs. By comparing with additional CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal spacing of each impingement array was decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that using cylindrical film holes near throat could benefit both aerodynamics and cooling.


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