Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1871-1883 ◽  
Author(s):  
WY Lin ◽  
YH Tsai ◽  
KM Hsiao
Keyword(s):  
Optimum Design ◽  
Angular Position ◽  
Angular Acceleration ◽  
Optimization Methods ◽  
Polynomial Coefficients ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Kinematic Performance ◽  
The Difference ◽  
Teaching Learning

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

A new indexing motion program for optimum designs of Geneva mechanisms with curved slots

2016 ◽  
Vol 231 (21) ◽  
pp. 3974-3986 ◽  
Author(s):  
WY Lin ◽  
YH Tsai ◽  
KM Hsiao
Keyword(s):  
Optimum Design ◽  
Angular Acceleration ◽  
Radius Of Curvature ◽  
Peak Acceleration ◽  
Design Variables ◽  
Order Polynomial ◽  
Hermite Interpolating Polynomial ◽  
Kinematic Performance ◽  
Fifth Order

A curved slotted Geneva mechanism can eliminate the adversely infinite angular jerks of the Geneva wheel and might reduce the peak angular acceleration of the Geneva wheel by using a proper indexing motion program. In the literature, the cycloidal, fifth-order polynomial and modified sine indexing motion programs are frequently used for curved slotted Geneva mechanisms. To achieve the better kinematic performance of the curved slotted Geneva wheel than that obtained using the above-mentioned indexing motion programs, a new indexing motion program based on the Hermite interpolating polynomial is proposed for an optimum design with the goals of minimizing the peak angular acceleration and eliminating the adversely infinite angular jerks. The domain of the indexing position function is divided into several segments. Each segment is termed an element, and both ends of each segment are termed nodes. The nodal values of the indexing position function and its derivatives are used as design variables. The position function for each element can be described using the Hermite interpolating polynomial and the design variables. The reason behind the use of the Hermite interpolating polynomial is that the design variables have the clear physical meanings. The four-level Hermite interpolating polynomial is used and two elements are sufficient to obtain the optimum results. In addition, the constraint regarding the radius of curvature of the profile of the inner slot is proposed to prevent sharp curvature of the profile of the inner slot. The findings show that there is a decline in the peak acceleration of the Geneva wheel with six curved slots for the optimum results obtained using the proposed indexing motion program by 33.4% and 24.3%, respectively, as compared with the cycloidal and modified sine indexing motion programs.

Airfoil Topology Optimization using Teaching-Learning based Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Dushhyanth Rajaram ◽  
Himanshu Akhria ◽  
S. N. Omkar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Spline Curves ◽  
Conceptual Design Phase ◽  
Design Variables ◽  
Heuristic Technique ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAE Aerodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.

Selection of Body Segment Parameters by Optimization Methods

1982 ◽  
Vol 104 (1) ◽  
pp. 38-44 ◽  
Author(s):  
C. L. Vaughan ◽  
J. G. Andrews ◽  
J. G. Hay
Keyword(s):  
Optimization Theory ◽  
Bilateral Symmetry ◽  
Optimization Methods ◽  
Body Segment ◽  
Kinematic Data ◽  
Design Variables ◽  
The Difference ◽  
Body Segment Parameters ◽  
Gamma Scanning ◽  
Selection Of

The selection of body segment parameters (BSPs) is a difficult yet essential task in many biomechanical studies. The methods used to date—cadaver, reaction board, mathematical modeling, gamma scanning, and kinematics—all have a number of drawbacks. The purpose of the present paper is to present an alternative method, based on kinematic data and optimization theory, for selecting BSPs. The design variables are the BSPs and the objective function to be minimized is based on the difference between calculated and measured distal extremity kinetics, while the equality constraints are based on Newtonian principles as well as bilateral symmetry of the BSPs. Three different activities are used to generate “optimal” sets of BSPs and these values are different, but not markedly so, from cadaver values. Further detailed investigation appears warranted.

scholarly journals Design optimization of a three-stage transmission using advanced optimization techniques

2019 ◽  
Vol 10 ◽  
pp. A8 ◽  
Author(s):  
Edmund S. Maputi ◽  
Rajesh Arora
Keyword(s):  
Geometric Model ◽  
Optimization Techniques ◽  
Stress Variation ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Machine Elements ◽  
Mechanical Devices ◽  
Compact Design ◽  
Teaching Learning ◽  
Intended Use

Gear transmission systems are very important machine elements and their failure can lead to losses or damage of other mechanical components that comprise a machine or device. Since gears are applied in numerous mechanical devices, there is need to design and subsequently optimize them for intended use. In the present work, two objectives, viz., volume and center distance, are minimized for a rotary tiller to achieve a compact design. Two methods were applied: (1) analytical method, (2) a concatenation of the bounded objective function method and teaching–learning-based optimization techniques, thereby improving the result by 44% for the former and 55% for the latter. Using a geometric model and previous literature, the optimal results obtained were validated with 0.01 variation. The influence of design variables on the objective functions was also evaluated using variation studies reflecting on a ranking according to objective. Bending stress variation of 12.4% was less than contact stress at 51% for a defined stress range.

Optimum Design of Flexible Mechanisms

1983 ◽  
Vol 105 (2) ◽  
pp. 267-272 ◽  
Author(s):  
Ce Zhang ◽  
H. T. Grandin
Keyword(s):  
Mechanism Design ◽  
Optimum Design ◽  
Optimality Criterion ◽  
Geometrical Parameters ◽  
Cross Sectional ◽  
Design Variables ◽  
Recursion Formulas ◽  
Kinematic Performance ◽  
Sectional Parameters ◽  
Flexible Mechanisms

In this paper the optimality criterion technique transplanted by Khan and his coworkers into mechanism design and the kinematical refinement technique proposed by the authors are combined into a novel procedure of optimum design of flexible mechanisms. Cross-sectional parameters are taken as the first group of design variables; a fully-stressed mechanism is obtained by using previous researchers’ recursion formulas which contain some improvements introduced by the authors. Geometrical parameters are used as the second group of design variables; a mechanism with improved criterion of kinematic performance is obtained by means of the kinematic refinement technique. The method presented is effective and steady. An example problem in the design of a four-bar path-generating mechanism is given to illustrate the procedure.

scholarly journals Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

2021 ◽  
Author(s):  
Ali Kaveh ◽  
Mohammad Iman Karimi Dastjerdi ◽  
Ataollah Zaerreza ◽  
Milad Hosseini
Keyword(s):  
Optimal Design ◽  
Optimum Design ◽  
Population Based ◽  
Colliding Bodies Optimization ◽  
Industrial Buildings ◽  
Enhanced Colliding Bodies Optimization ◽  
Teaching Learning Based Optimization ◽  
Fire Stations ◽  
Teaching Learning ◽  
Discrete Optimum

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

Competitive Learning: A New Meta-Heuristic Optimization Algorithm

2018 ◽  
Vol 27 (08) ◽  
pp. 1850035 ◽  
Author(s):  
Afshin Afroughinia ◽  
Reihaneh Kardehi Moghaddam
Keyword(s):  
Optimization Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Optimization Methods ◽  
Competitive Learning ◽  
Heuristic Optimization ◽  
Computational Speed ◽  
Teaching Learning Based Optimization ◽  
Improving Accuracy ◽  
Teaching Learning ◽  
Education Process

This work proposes a new powerful meta-heuristic optimization algorithm in education process called Competitive Learning (CLA). The algorithm is benchmarked on 8 well-known test functions, and the results are verified by a comparative study with some meta-heuristic optimization methods including: Imperialist Competitive Algorithm (ICA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Algorithm (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Analyzing the findings, it is shown that the CLA algorithm is able to provide more accurate results than other well-known meta-heuristic ones. Also, those results applied to famous unimodal and multimodal benchmarks show CLA is efficient in improving accuracy as well as computational speed.

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