Materials Design Using Computational Intelligence Techniques: 1st Edition (Hardback) book cover

Materials Design Using Computational Intelligence Techniques

1st Edition

By Shubhabrata Datta

CRC Press

158 pages | 135 B/W Illus.

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Hardback: 9781482238327
pub: 2016-10-18
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Several statistical techniques are used for the design of materials through extraction of knowledge from existing data banks. These approaches are getting more attention with the application of computational intelligence techniques. This book illustrates the alternative but effective methods of designing materials, where models are developed through capturing the inherent correlations among the variables on the basis of available imprecise knowledge in the form of rules or database, as well as through the extraction of knowledge from experimental or industrial database, and using optimization tools.


"Intelligent computing techniques of diverse kinds are now very significantly influencing novel material design. In the current state of technology and the industrial requirements, these are some vital tools to make the design efficient, along with a lesser dependence on expensive and cumbersome experimentation. Such techniques are now ubiquitous and are widely used across disciplines. A good research based book focusing exclusively on their applications pertinent to the materials area is not very easy to locate. For that this very impressive book is just in time. The wide gamut of algorithms and their applications that it presents is very impressive. It is nice to see widely used Finite element technique and evolutionary techniques like Genetic Programming inside the same cover along with their pertinent applications. This book will be very useful for the scholars and researchers in this area and should be very useful for the classroom teaching of an advanced course in the area of Computational Materials Science."

— Nirupam Chakraborti, Indian Institute of Technology, India

Table of Contents



Computer and design

Materials design: past, present and future

A brief overview of traditional approaches to materials design

Density function theory and other ab initio modeling techniques

Molecular dynamics

Thermodynamic modeling

Phase field simulation

CALPHAD approach

Finite element and other similar methods

Multi-scale modeling and Integrated Computational Materials Engineering

Statistics and data mining concepts

Basic concepts of statistics

An overview of statistical modeling in materials domain

Concept of data mining

Mining materials data and informatics based design

Principles of neural network and other soft modeling techniques

Artificial neural network and support vector machine

Genetic programming

Applications of soft modeling techniques in materials field

A few case studies

Suitability as a materials design tool

Knowledge extraction using rough and fuzzy set theories

Fuzzy logic

Rule extraction using fuzzy clustering

Rough set theory

Case studies of successful applications

Potential future applications

Handling imprecise knowledge through fuzzy inference system

Imprecise knowledge in materials engineering

Fuzzy inference system

Neuro-fuzzy inference system

Case studies

Evolutionary algorithm for designing materials

Optimization for designing new materials

Evolutionary optimization algorithms

Multi-objective optimization

An overview of optimization based materials design

Technique blends to suit a materials system

Soft computing tools in tandem

Applications for materials design

Hybridizing with traditional approaches

Semi-empirical approaches for materials modeling

Hybridizing preciseness and impreciseness in materials knowledge

Designing microstructure

Microstructure simulation tools

Non-traditional designing tailored microstructure

Hybridization with traditional modeling

Concluding remarks

Traditional vs. non-traditional approaches: competitive or complementary?

Information to perception

Handling uncertainty in materials design

About the Originator

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