Artificial neural networks (ANN) can provide new insight into the
study of composite materials and can normally be combined with
other artificial intelligence tools such as expert system, genetic
algorithm, and fuzzy logic. Because research on this field is very
new, there is only a limited amount of published literature on the
subject. Compiling information from diverse sources, Composite
Materials Technology: Neural Network Applications fills the void in
knowledge of these important networks, covering composite
mechanics, materials characterization, product design, and other
important aspects of polymer matrix composites. Light weight,
corrosion resistance, good stiffness and strength properties, and
part consolidation are just some of the reasons that composites are
useful in areas including civil engineering and structure, chemical
processing, management, agriculture, space study, and
manufacturing. ANN has already been used to carry out design
prediction, mechanical property prediction, and selection processes
in the evolution of composites, but although it has already been
used with great success in various branches of scientific and
technological research, it is still in the nascent stage of its
development. Featuring contributions from leading researchers
throughout the world, this book is divided into four parts,
starting with an introduction to neural networks and a review of
existing literature on the subject. The text then covers structural
health monitoring and damage detection in composites, addresses
mechanical properties, and discusses design, analysis, and
materials selection. Training, testing, and validation of
experimental data were carried out to optimize the results
presented in the book. This book will be an important aid to
researchers as they work on the future implementation of ANN in
industries such as aerospace, automotive, marine, sporting goods,
furniture, and electronics and communication.
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