Mechanical Design Optimization Using Advanced Optimization Techniques / R. Venkata Rao , Vimal J. Savsani
Material type:
- 9781447127482
- 9781447159780
- T353 R36 2012
Item type | Current library | Call number | Copy number | Status | Barcode | |
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Fayza Aboulnaga Central Library | مكتبة فايزة أبو النجا المركزية بالحرم الجامعي | T353 .R36 2012 (Browse shelf(Opens below)) | C. 1 | Available | 10012033 | |
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Fayza Aboulnaga Central Library | مكتبة فايزة أبو النجا المركزية بالحرم الجامعي | T353 .R36 2012 (Browse shelf(Opens below)) | C. 2 | Available | 10012001 |
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T174.7 .W65 2012 Understanding the Nanotechnology Revolution / | T353 .E617 2012 Engineering drawing & design / | T353 .R36 2012 Mechanical Design Optimization Using Advanced Optimization Techniques / | T353 .R36 2012 Mechanical Design Optimization Using Advanced Optimization Techniques / | T353 .T43 2014 Technical Drawing with Engineering Graphics / | T353 .T43 2014 Technical Drawing with Engineering Graphics / | T353 .T43 2014 Technical Drawing with Engineering Graphics / |
Includes Index
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
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