Stackable 5R Chains With Multiple End-Effectors: Architecture and Optimum Synthesis

2006 ◽  
Author(s):  
Ahmad Smaili ◽  
Bachir Chaaya
Keyword(s):  
Genetic Algorithm ◽  
Spot Welding ◽  
Dimensional Synthesis ◽  
Gradient Search ◽  
Search Methods ◽  
Optimum Synthesis ◽  
Welding Operation ◽  
End Effectors

Presented in this paper is a 4-dof robomech consisting of a stack of two five-bar (5R) chains with multiple end effectors to perform two distinct yet coordinated tasks simultaneously. The over-constrained kinematics chain cannot traverse continuous trajectories but may be synthesized to move the end effectors through a set of desired locations. The article elaborates on the architecture of the proposed robomech, establishes its kinematics relations and constraints, and provides dimensional synthesis scheme based on genetic algorithm and gradient search methods. A case study featuring the robomech performing spot welding operation along two distinct trajectories is included to demonstrate the applicability of the proposed robomech.

A Triad-Based Two-DOF Robomech: Architecture and Optimum Synthesis

2005 ◽  
Author(s):  
Ahmad Smaili ◽  
Bachir Chaaya
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Kinematic Chain ◽  
Dimensional Synthesis ◽  
Gradient Search ◽  
Search Methods ◽  
Optimum Synthesis ◽  
End Effectors

Presented in this paper is a 2-dof robomech that carries two end effectors to perform two functions simultaneously. The robomech has a 7R architecture consisting of two triad wings connected to form two cells. The over-constrained kinematic chain cannot traverse continuous trajectories but may be synthesized to move the end effectors through a set of desired locations. The article presents the architecture of the proposed robomech, establishes its kinematic relations and constraints, and provides dimensional synthesis scheme based on genetic algorithm and gradient search methods. Two case studies are included to demonstrate the applicability of the proposed robomech.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

A New Approach for Exact/Approximate Point Synthesis of Planar Mechanisms

2005 ◽  
Author(s):  
Ahmad Smaili ◽  
Nadim Diab
Keyword(s):  
Synthesis Method ◽  
Dimensional Synthesis ◽  
Gradient Search ◽  
Planar Mechanisms ◽  
Simple Method ◽  
New Approach ◽  
Optimum Synthesis ◽  
Approximate Synthesis ◽  
Optimum Solution ◽  
Synthesis Approach

The aim of this article is to provide a simple method to solve the mixed exact-approximate dimensional synthesis problem of planar mechanism. The method results in a mechanism that can traverse a closed path with the choice of any number of exact points while the rest are approximate points. The algorithm is based on optimum synthesis rather than on precision position methods. Ant-gradient search is applied on an objective function based on log10 of the error between the desired positions and those generated by the optimum solution. The log10 function discriminates on the side of generating miniscule errors (on the order of 10−14) at the exact points while allowing for higher errors at the approximate positions. The algorithm is tested by way of five examples. One of these examples was used to test exact/approximate synthesis method based on precision point synthesis approach.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Design and Evaluation of Usable Systems

2002 ◽  
Vol 46 (3) ◽  
pp. 367-371
Author(s):  
John F. McGrew
Keyword(s):  
Genetic Algorithm ◽  
Change Management ◽  
Management System ◽  
Design Method ◽  
The Other ◽  
Design Team ◽  
Style Analysis ◽  
Decision Style ◽  
Parallel Design

This paper discusses a case study of a design and evaluation of a change management system at a large Telecommunications Corporation. The design and evaluation were done using the facilitated genetic algorithm (a parallel design method) and user decision style analysis. During the facilitated genetic algorithm the design team followed the procedure of the genetic algorithm. Usability was evaluated by applying user decision style analysis to the designed system. The design is compared with an existing system and with one designed by an analyst. The change management system designed by the facilitated genetic algorithm took less time to design and decision style analysis indicated it would be easier to use than the other two systems.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

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