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2012 | Buch

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Mechanical Design Optimization Using Advanced Optimization Techniques

verfasst von: R. Venkata Rao, Vimal J. Savsani

Verlag: Springer London

Buchreihe : Springer Series in Advanced Manufacturing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Mechanical design includes an optimization process in which designers always consider objectives such as strength, deflection, weight, wear, corrosion, etc. depending on the requirements. However, design optimization for a complete mechanical assembly leads to a complicated objective function with a large number of design variables. It is a good practice to apply optimization techniques for individual components or intermediate assemblies than a complete assembly. Analytical or numerical methods for calculating the extreme values of a function may perform well in many practical cases, but may fail in more complex design situations. In real design problems, the number of design parameters can be very large and their influence on the value to be optimized (the goal function) can be very complicated, having nonlinear character. In these complex cases, advanced optimization algorithms offer solutions to the problems, because they find a solution near to the global optimum within reasonable time and computational costs.

Mechanical Design Optimization Using Advanced Optimization Techniques presents a comprehensive review on latest research and development trends for design optimization of mechanical elements and devices. Using examples of various mechanical elements and devices, the possibilities for design optimization with advanced optimization techniques are demonstrated. Basic and advanced concepts of traditional and advanced optimization techniques are presented, along with real case studies, results of applications of the proposed techniques, and the best optimization strategies to achieve best performance are highlighted. Furthermore, a novel advanced optimization method named teaching-learning-based optimization (TLBO) is presented in this book and this method shows better performance with less computational effort for the large scale problems.

Mechanical Design Optimization Using Advanced Optimization Techniques is intended for designers, practitioners, managers, institutes involved in design related projects, applied research workers, academics, and graduate students in mechanical and industrial engineering and will be useful to the industrial product designers for realizing a product as it presents new models and optimization techniques to make tasks easier, logical, efficient and effective.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Mechanical design includes an optimization process in which designers always consider certain objectives such as strength, deflection, weight, wear, corrosion, etc. Depending on the requirements. However, design optimization for a complete mechanical assembly leads to a complicated objective function with a large number of design variables. So it is a good practice to apply optimization techniques for individual components or intermediate assemblies than a complete assembly. For example, in an automobile power transmission system, optimization of gearbox is computationally and mathematically simpler than the optimization of complete system.

R. Venkata Rao, Vimal J. Savsani

Chapter 2. Advanced Optimization Techniques

Abstract

This chapter presents the details of existing optimization algorithms such as Genetic Algorithm (GA), Artificial Immune Algorithm (AIA), Differential Evolution (DE), Biogeography-Based Optimization (BBO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Elements Algorithm (HEA), Shuffled Frog Leaping Algorithm (SFLA) and Grenade Explosion Algorithm (GEA). The step-by-step procedure of implementation of each algorithm is presented. Some modifications made to improve the performance of PSO, ABC and HEA are also presented. In addition, four different hybrid algorithms are presented by keeping ABC as the base algorithm. The hybrid algorithms presented are: HPABC (Hybrid Particle swarm-based Artificial Bee Colony), HBABC (Hybrid Biogeography-based Artificial Bee Colony), HDABC (Hybrid Differential evolution-based Artificial Bee Colony) and HGABC (Hybrid Genetic algorithm-based Artificial Bee Colony).

R. Venkata Rao, Vimal J. Savsani

Chapter 3. Mechanical Design Optimization Using the Existing Optimization Techniques

Abstract

This chapter presents the applications of advanced optimization algorithms such as ABC, PSO, DE, BBO and AIA to the design optimization of mechanical elements such as a simple gear train, radial ball bearing, Belleville spring, multi-plate disc clutch brake, robot gripper, hydrostatic thrust bearing and a four stage gear train. The objective functions, design variables and the constraints of each of the design optimization problems are described. The results of application of the advanced optimization algorithms are presented and the performance comparison is made between the algorithms. The results are also compared with the results given by the previous researchers.

R. Venkata Rao, Vimal J. Savsani

Chapter 4. Applications of Modified Optimization Algorithms to the Unconstrained and Constrained Problems

Abstract

This chapter presents the applications of the modified PSO, HEA and ABC algorithms. Thirteen unconstrained and twenty-four constrained benchmark problems available in the literature are considered to check the performance of the modified algorithms. In addition, different mechanical element design optimization problems such as design of a simple gear train, radial ball bearing, Belleville spring, multi-plate disc clutch brake, robot gripper, hydrostatic thrust bearing, a four-stage gear train, pressure vessel, welded beam, tension/compression spring, speed reducer, stiffened cylindrical shell, step cone pulley, screw jack, C-clamp, hydrodynamic bearing, cone clutch, cantilever support, hydraulic cylinder and a planetary gear train are presented and the effectiveness of the applications of the modified algorithms is checked. It is observed that the modifications in PSO and HEA are effective than their basic versions. Modifications in ABC are not so effective for the constrained benchmark functions but are found effective for the unconstrained benchmark functions and mechanical design problems.

R. Venkata Rao, Vimal J. Savsani

Chapter 5. Applications of Hybrid Optimization Algorithms to the Unconstrained and Constrained Problems

Abstract

This chapter presents the applications of four different hybrid algorithms to the unconstrained and constrained benchmark functions and also to the mechanical element design optimization problems of a simple gear train, radial ball bearing, Belleville spring, multi-plate disc clutch brake, robot gripper, hydrostatic thrust bearing, a four stage gear train, pressure vessel, welded beam, tension/compression spring, speed reducer, stiffened cylindrical shell, step-cone pulley, screw jack, C-clamp, hydrodynamic bearing, cone clutch, cantilever support, hydraulic cylinder and a planetary gear train. Four different optimization algorithms such as PSO, BBO, DE and GA are chosen to hybridize them with ABC algorithm. Comparison of the overall performance of hybrid algorithms with the basic algorithms is made and it is observed that hybridization of ABC and PSO is effective than the basic PSO algorithm. For searching the best solutions, hybridization of ABC with PSO and GA is not so effective than the basic ABC. Hybridization of ABC with BBO and DE is effective than the basic ABC in finding the best solutions. Moreover, Hybridization of ABC with DE is very effective than the basic ABC and other algorithms.

R. Venkata Rao, Vimal J. Savsani

Chapter 6. Development and Applications of a New Optimization Algorithm

Abstract

All the nature-inspired algorithms such as genetic algorithm (GA), PSO, BBO, ABC, DE, etc. require algorithm parameters to be set for their proper working. Proper selection of parameters is essential for the searching of the optimum solution by these algorithms. A change in the algorithm parameters changes the effectiveness of the algorithms. To avoid this difficulty, an optimization method named ‘Teaching–Learning-Based Optimization (TLBO)’ is presented in this chapter. This method works on the effect of influence of a teacher on learners. The performance of the proposed TLBO method is checked with the recent and well-known optimization algorithms such as GA, ABC, PSO, HS, DE and hybrid algorithms by experimenting with different constrained and unconstrained benchmark problems and mechanical element design optimization problems with different characteristics. The effectiveness of TLBO method is also checked for different performance criteria, like success rate, mean solution, average function evaluations required, convergence rate, etc. The results show better performance of TLBO method over other natured-inspired optimization methods for the considered benchmark functions and mechanical element design optimization problems. Also, the TLBO method shows better performance with less computational effort for the large-scale problems, i.e. problems with high dimensions.

R. Venkata Rao, Vimal J. Savsani

Chapter 7. Design Optimization of Selected Thermal Equipment Using Advanced Optimization Techniques

Abstract

This chapter presents the details of design optimization of some selected thermal equipment such as a two-stage thermoelectric cooler (TEC), a shell and tube heat exchanger (STHE) and a heat pipe. The TLBO algorithm is applied successfully to the multi-objective optimization of a two-stage TEC considering two conflicting objectives: cooling capacity and COP. Two different configurations of TECs, electrically separated and electrically connected in series, are investigated for the optimization. The ability of the TLBO algorithm is demonstrated and the performance of the TLBO algorithm is compared with the performance of GA. Three case studies of the shell and tube heat exchanger (STHE) optimization are attempted by the shuffled frog leaping algorithm (SFLA) and the results of optimization are found better than those reported by GA and PSO algorithms. In the case of design optimization of a heat pipe, the best results produced by grenade explosion method (GEM) are compared with the generalized extremal optimization (GEO) algorithm.

R. Venkata Rao, Vimal J. Savsani

Chapter 8. Conclusions

Abstract

Mechanical elements such as gears, bearings, clutches, springs, power screws, hydraulic cylinders, etc., are widely used in machine tools, transmission systems, material handling equipments, automobiles, etc. Design of these mechanical elements includes an optimization process in which the designers consider certain objectives such as strength, deflection, weight, wear, corrosion, etc. depending on the requirements. It is required to optimize such mechanical elements to make the whole assembly efficient and cost effective.

R. Venkata Rao, Vimal J. Savsani

Backmatter

Titel: Mechanical Design Optimization Using Advanced Optimization Techniques
verfasst von: R. Venkata Rao
Vimal J. Savsani
Verlag: Springer London
Electronic ISBN: 978-1-4471-2748-2
Print ISBN: 978-1-4471-2747-5
DOI: https://doi.org/10.1007/978-1-4471-2748-2

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