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- Shows the reader advanced methods for tuning finite element simulations to measured data
- Features a dual use of expectation maximization and response surface methods that compensates for computational complexity
- Includes robust optimization techniques that reconcile local with global models

Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include:

multi-layer perceptron neural networks for real-time FEM updating;

particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models;

simulated annealing to put the methodologies into a sound statistical basis; and

response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity.

Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating. Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.

Content Level Professional/practitioner

Keywords: STATISTICA - algorithm - algorithms- design - finite element method - genetic algorithms - linear optimization - mechanical engineering - optimization - simulation - vibration

 Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: • multi-layer perceptron neural networks for real-time FEM updating; • particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; • simulated annealing to put the methodologies into a sound statistical basis; and • response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity. Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating. Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.

Mathematical analysis --- Engineering sciences. Technology --- eindige elementen

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The selection of automobile body materials is fundamental to the choice of fabrication method, and the characteristics and performance of the final vehicle or component. The factors behind these choices comprise some of the key technological and design issues facing automotive engineers today. Materials for Automobile Bodies brings together a wealth of information on automotive materials and material technologies to provide designers and vehicle body engineers with both a solid grounding and a quick reference to inform their material choices. Coverage includes materials processing, formability, welding and joining, anti-corrosion technologies, plus a comprehensive consideration of the implications of materials selection on these processes. 

Dealing with the whole assembly process from raw material to production, right through to recycling at the end of a vehicles life, this book is the essential resource for practising engineers, designers, analysts and students involved in the design and specification of motor vehicle bodies and components. 

    Up-to-date detailed information on contemporary autobody materials, incorporating the explanation often lacking in other data-focused resources 
    Includes informative and insightful case studies on the materials and processing choices of major OEMs, including Honda, BMW and Audi 
    Now with more on geographical supply and usage trends, environmental concerns and end of life disassembly considerations, and how these affect selection choices

 The selection of automobile body materials is fundamental to the choice of fabrication method, and the characteristics and performance of the final vehicle or component. The factors behind these choices comprise some of the key technological and design issues facing automotive engineers today. Materials for Automobile Bodies brings together a wealth of information on automotive materials and material technologies to provide designers and vehicle body engineers with both a solid grounding and a quick reference to inform their material choices. Coverage includes materials processing, formability, welding and joining, anti-corrosion technologies, plus a comprehensive consideration of the implications of materials selection on these processes. Dealing with the whole assembly process from raw material to production, right through to recycling at the end of a vehicle's life, this book is the essential resource for practising engineers, designers, analysts and students involved in the design and specification of motor vehicle bodies and components. Up-to-date detailed information on contemporary autobody materials, incorporating the explanation often lacking in other data-focused resources Includes informative and insightful case studies on the materials and processing choices of major OEMs, including Honda, BMW and Audi Now with more on geographical supply and usage trends, environmental concerns and end of life disassembly considerations, and how these affect selection choices.

Materials sciences --- Non-cutting processes --- Motorcars engineering --- Production management --- eindige elementen --- BMW (auto's) --- Honda (auto's) --- DFA (design for assembly) --- lassen --- productiebesturing --- assemblage (technologie) --- recyclage --- materiaalkeuze --- metaalbewerking --- auto's --- Audi (auto's)