Manufacturing & Optimization Problems of Electromagnetic Devices.

1996 ◽  
Author(s):  
E. J. Barouch
Keyword(s):  
Optimization Problems ◽  
Electromagnetic Devices ◽  
Manufacturing Optimization

scholarly journals Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 494
Author(s):  
Ekaterina Andriushchenko ◽  
Ants Kallaste ◽  
Anouar Belahcen ◽  
Toomas Vaimann ◽  
Anton Rassõlkin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Permanent Magnet ◽  
Optimization Problems ◽  
Optimization Method ◽  
Taguchi Optimization ◽  
Torque Density ◽  
Electromagnetic Devices ◽  
3D Printed ◽  
Manufacturing Techniques

In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup.

Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters

2015 ◽  
pp. 536-563 ◽  
Author(s):  
Arindam Majumder ◽  
Abhishek Majumder
Keyword(s):  
Standard Deviation ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Integrated Approach ◽  
Multi Objective ◽  
Research Areas ◽  
Manufacturing Optimization ◽  
Logical Method ◽  
Deviation Method

Multi-objective optimization is one of the most popular research areas in the world of manufacturing. It concerns the manufacturing optimization problems involving more than one optimization simultaneously, but in this present scenario, it is becoming very tough to solve a manufacturing-related multi-objective problem as no logical method has been developed in assignment of response individual weight. Therefore, to tackle this problem, this chapter proposes a new integrated approach by combining Standard Deviation Method with Particle Swarm Optimization. Two examples of optimizing the advanced manufacturing process parameters are performed to test the proposed approach. The examples considered for this approach are also attempted using other established optimization techniques such as Desirability-based RSM and SDM-GA. The results verify the effectiveness of the proposed approach during multi-objective manufacturing process parameter optimization.

A Comparative Study of Firefly Algorithm and Shuffled Frog-Leaping Algorithm for Constrained Free and Manufacturing Optimization Problems

2013 ◽  
Vol 457-458 ◽  
pp. 618-623
Author(s):  
Pasura Aungkulanon ◽  
Isaree Srikun ◽  
Lakkana Ruekkasem
Keyword(s):  
Manufacturing Process ◽  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Finite Sequence ◽  
Shuffled Frog Leaping Algorithm ◽  
Industrial Systems ◽  
Manufacturing Optimization ◽  
Mean And Variance ◽  
Shuffled Frog Leaping

Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups; optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.

scholarly journals Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems

2017 ◽  
Vol 5 (2) ◽  
pp. 249-273 ◽  
Author(s):  
Rizk M. Rizk-Allah
Keyword(s):  
Engineering Design ◽  
Search Strategy ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Design Problems ◽  
Local Optima ◽  
Manufacturing Optimization ◽  
Engineering Design Problems ◽  
Orthogonal Search ◽  
Sine Cosine Algorithm

Abstract This paper presents a new algorithm based on hybridizing the sine cosine algorithm (SCA) with a multi-orthogonal search strategy (MOSS), named multi-orthogonal sine cosine algorithm (MOSCA), for solving engineering design problems. The proposed MOSCA integrates the advantages of the SCA and MOSS to eliminate SCA's disadvantages, like unbalanced exploitation and the trapping in local optima. The proposed MOSCA works in two stages, firstly, the SCA phase starts the search process to enhance exploration capability. Secondly, the MOSS phase starts its search from SCA found so far to boost the exploitation tendencies. In this regard, MOSS phase can assist SCA phase to search based on deeper exploration/exploitation patterns as an alternative. Therefore, the MOSCA can be more robust, statistically sound, and quickly convergent. The performance of the MOSCA algorithm is investigated by applying it on eighteen benchmark problems and four engineering design problems. The experimental results indicate that MOSCA is a promising algorithm and outperforms the other algorithms in most cases. Highlights MOSCA is presented to solve design and manufacturing optimization problems efficiently. MOSCA is based on two phases namely, sine cosine algorithm (SCA) and multi-orthogonal search strategy (MOSS). The integrated MOSCA enhances exploration tendency and exploitation capability. The MOSCA can be more robust, statistically sound, and quickly convergent. New approach produced successful results compared to the literature studies.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Sign in / Sign up

Export Citation Format

Share Document