Muutke küpsiste eelistusi

E-raamat: Materials Design Using Computational Intelligence Techniques

(SRM University, Chennai)
  • Formaat: 184 pages
  • Ilmumisaeg: 26-Oct-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781315355689
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 59,79 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Materials Design Using Computational Intelligence TechniquesSuurem pilt
  • Formaat: 184 pages
  • Ilmumisaeg: 26-Oct-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781315355689
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

Arvustused

"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

List of Figures
xi
List of Software and Web Resources
xvii
Preface xix
Acknowledgements xxiii
Author xxv
1 Introduction
1(12)
1.1 Computers and Design
1(4)
1.2 Computational Intelligence
5(4)
1.3 Materials Design: Past, Present and Future
9(4)
References
11(2)
2 Conventional Approaches to Materials Design
13(12)
2.1 Density Functional Theory
14(1)
2.2 Molecular Dynamics
15(1)
2.3 Thermodynamic Modelling and the CALPHAD Approach
16(2)
2.4 Phase-Field Simulation
18(1)
2.5 Cellular Automata and Monte Carlo Simulations
19(2)
2.6 Finite Element and Other Similar Methods
21(1)
2.7 Multiscale Modelling and Integrated Computational Materials Engineering
21(4)
References
23(2)
3 Statistics and Data Mining Concepts in Materials Design
25(14)
3.1 An Overview of Statistical Modelling in the Materials Field
25(2)
3.2 Concept of Data Mining
27(2)
3.3 Mining Materials Data and Informatics-Based Design
29(5)
3.3.1 Hot Rolled Steel Plate Classification
29(4)
3.3.2 High-Temperature Superconductors
33(1)
3.4 Problems and Prospects
34(5)
References
36(3)
4 Neural Networks and Genetic Programming for Materials Modelling
39(26)
4.1 Artificial Neural Networks
39(5)
4.2 Genetic Programming
44(1)
4.3 Applications in Materials Engineering
45(9)
4.3.1 In Situ Prediction of Porosity of Nanostructured Porous Silicon
45(3)
4.3.2 Steel Plate Processing
48(2)
4.3.3 Strength of HSLA Steel Using a Customised Network
50(2)
4.3.4 An Example of Unsupervised Learning
52(1)
4.3.5 Example of an Application of Genetic Programming
53(1)
4.4 Suitability as Materials Design Tools
54(11)
References
63(2)
5 Knowledge Extraction Using Fuzzy and Rough Set Theories
65(20)
5.1 Fuzzy Logic
66(2)
5.1.1 Rule Extraction Using Clustering Methods
67(1)
5.2 Rough Set Theory
68(2)
5.3 Case Studies of Successful Applications
70(12)
5.3.1 Friction Stir Welding of Al Alloys
70(1)
5.3.2 Ultrasonic Drilling of Ceramic Materials
71(2)
5.3.3 Mechanical Properties of Ti Alloys
73(7)
5.3.4 Mechanical Properties of TRIP Steel
80(2)
5.4 Potential Future Applications
82(3)
References
83(2)
6 Handling Imprecise Knowledge through Fuzzy Inference Systems
85(22)
6.1 Fuzzy Inference Systems
86(2)
6.2 Adaptive Neuro-FIS
88(2)
6.3 Case Studies
90(15)
6.3.1 Modelling HSLA Steel
90(4)
6.3.2 Modelling TRIP-Aided Steel
94(5)
6.3.3 Machining of Ti Alloy
99(3)
6.3.4 Ageing of Cu-Bearing Steel
102(3)
6.4 Uncertainty and Imprecision in Materials Systems
105(2)
References
106(1)
7 Evolutionary Algorithms for Designing Materials
107(16)
7.1 Optimisation for Designing New Materials
107(3)
7.1.1 Multiobjective Optimisation
109(1)
7.2 Evolutionary Optimisation Algorithms
110(2)
7.3 Case Studies of Optimisation-Based Materials Design
112(8)
7.3.1 Optimisation of Cold Rolling Mills
113(1)
7.3.2 Gas Injection in Steelmaking
114(1)
7.3.3 Strength and Ductility Optimisation of Low-Carbon Steel
114(3)
7.3.4 Optimisation of the PET Reactor Using DE
117(3)
7.4 Issues in the Optimisation Approach
120(3)
References
121(2)
8 Using Computational Intelligence Techniques in Tandem
123(22)
8.1 Designing Materials with ANN Models as the Objective Function
123(15)
8.1.1 Designing Steel with Custom-Made Properties
124(5)
8.1.2 Designing Novel Age-Hardenable Aluminium Alloys
129(6)
8.1.3 Optimum Processing for Better Shape Memory Effect of Nitinol
135(3)
8.2 Polymer Composite Design with Fuzzy Models as the Objective Function
138(4)
8.3 Other Possible Approaches
142(3)
References
143(2)
9 Concluding Remarks
145(8)
9.1 Conventional versus CI-Based Materials Design
145(1)
9.2 Microstructure
146(1)
9.3 Green Design
147(1)
9.4 Handling Uncertainty
147(1)
9.5 Robust Solution
148(1)
9.6 DIKUW Hierarchy
149(4)
References
150(3)
Index 153
Shubhabrata Datta
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97813153556896e.html
Märksõnad: