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Optimal Design Exploiting 3D Printing and Metamaterials [Kõva köide]

Edited by (ód University of Technology, Institute of Mechatronics and Information Systems, Poland), Edited by (University of Pavia, Department of Electrical, Computer and Biomedical Engineering, Italy)
  • Formaat: Hardback, 330 pages, kõrgus x laius: 234x156 mm
  • Sari: Manufacturing
  • Ilmumisaeg: 11-Feb-2022
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 183953351X
  • ISBN-13: 9781839533518
Teised raamatud teemal:
Optimal Design Exploiting 3D Printing and MetamaterialsSuurem pilt
  • Formaat: Hardback, 330 pages, kõrgus x laius: 234x156 mm
  • Sari: Manufacturing
  • Ilmumisaeg: 11-Feb-2022
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 183953351X
  • ISBN-13: 9781839533518
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781839533518.html
Märksõnad:

The key theme of this book is an exploration of how recent advances across three related scientific fields are intertwined - the developments in metamaterials, the automated optimal design of innovative electronic, electromagnetic and mechatronic devices, and 3D printing.

Developments in the field of automated optimal design have enabled the design of innovative electronic, electromagnetic and mechatronic devices, but there is a risk that design uncertainties and fabrication tolerances dictated by conventional manufacturing techniques will limit the practical synthesis and industrial realisation of these novel designs. The solution might be found in new manufacturing possibilities offered by 3D printing technologies and techniques for the fabrication of conductive layers in low and high frequency applications.

The book approaches the topic from several perspectives, including the design of 3D fields, advances in shape synthesis, the role of additive manufacturing in synthesising metamaterials and manipulating ferromagnetic materials, and the steps from numerical models to printed mechatronic devices. A final chapter discusses design challenges and opportunities in industrial settings.

Led by two expert editors, with contributions from authors with a range of backgrounds across academia and industrial research, this book provides key information for researchers, advanced students and industry professionals in advanced manufacturing, mechatronics, and electrical and electronic engineering.



This book explores the potential for combining recent advances from three fields - metamaterials, automated optimal design and 3D printing. The role of metamaterials in innovative device design is explored, and the development of 3D printing techniques for successfully fabricating new devices from these materials is examined.

About the editors xi
Introduction 1(4)
1 Innovative materials, computational methods and their disruptive effects 5(52)
Massimo Guarnieri
1.1 Materials that changed the world
5(25)
1.1.1 Ancient disruptive materials
5(11)
1.1.2 Materials of the industrial revolution
16(14)
1.2 Computing machines and computers
30(12)
1.2.1 Ancient mechanical computing
30(1)
1.2.2 Mechanical calculators
30(3)
1.2.3 Mechanical and electromechanical computers
33(2)
1.2.4 Wartime electronic computers
35(2)
1.2.5 Generations of electronic computers
37(2)
1.2.6 Supercomputers
39(2)
1.2.7 Software
41(1)
1.3 Numerical methods
42(5)
1.3.1 Numerical methods in antiquity
42(1)
1.3.2 Numerical methods in the early modem period
43(1)
1.3.3 Numerical methods in the modem period
43(1)
1.3.4 Numerical methods in the twentieth century
44(2)
1.3.5 Computerized numerical methods
46(1)
1.4 Numerical optimization
47(1)
1.4.1 Deterministic optimization
47(1)
1.4.2 Stochastic optimization
47(1)
1.5 Numerical models for continuum models
48(4)
1.5.1 FDMs for ODEs and PDEs (1920s)
48(1)
1.5.2 FEM for PDEs (1940s)
49(1)
1.5.3 Other methods (1960s)
50(1)
1.5.4 Key developments in computational electromagnetics
51(1)
References
52(5)
2 Advances and trends in design optimisation 57(42)
Jan K. Sykulski
2.1 The hierarchical design paradigm
58(1)
2.2 The 'no free lunch' theorem
59(1)
2.3 Surrogate modelling
60(1)
2.4 The concept of a robust design
61(1)
2.5 The advances in computational electromagnetics
62(1)
2.6 Computer-aided design
63(1)
2.7 Single- and multi-objective optimisation
64(2)
2.8 Pareto optimisation
66(1)
2.9 Balancing exploitation and exploration
67(1)
2.10 Kriging techniques
67(2)
2.11 Numerical experiments using test functions
69(1)
2.12 An engineering example
70(7)
2.13 A brief review of nature inspired algorithms
77(1)
2.14 The challenge of large data sets
78(1)
2.15 Points aggregation techniques
79(2)
2.16 Design sensitivity aspect
81(2)
2.17 Future trends
83(10)
2.17.1 Model order reduction
84(4)
2.17.2 Deep learning
88(3)
2.17.3 Cloud computing
91(1)
2.17.4 Multi-physics
92(1)
2.18 Benchmarking
93(2)
2.19 Concluding remarks
95(1)
References
96(3)
3 Free-form optimal design in electromagnetism exploiting 3D printing 99(40)
Paolo Di Barba
Roberto Galdi
3.1 Introduction
99(1)
3.2 Inverse problems and optimal shape design
99(3)
3.3 State of the art
102(3)
3.4 A comparative view of methods
105(2)
3.5 Free-form optimisation
107(1)
3.6 A simple algorithm for dielectric design
108(7)
3.6.1 Dielectric design oriented free-form optimisation
110(3)
3.6.2 Optimisation results
113(2)
3.7 A posteriori overview of optimal shape design
115(2)
3.8 An enabling technology: towards Industry 4.0, level by level
117(2)
3.9 An overview of technologies
119(5)
3.9.1 A technology that could have in the near future a single limit: imagination
119(1)
3.9.2 Fused deposition modelling
120(1)
3.9.3 Stereolithography and digital light processing
121(1)
3.9.4 Selective laser sintering
122(1)
3.9.5 Multi jet fusion
123(1)
3.9.6 Material jetting printing
123(1)
3.9.7 Electron beam melting
123(1)
3.9.8 Binder jetting
124(1)
3.9.9 3D multi-material printing
124(1)
3.10 Materials vs meta-materials
124(1)
3.11 3D printing: a practical implementation
125(3)
3.12 Recent FDM experiences in electromagnetism
128(1)
3.13 Pros and cons of the new approach
128(1)
3.14 3D printing oriented optimal design
129(1)
3.15 Driving the slicing: process-oriented coding
130(2)
3.16 Technology-related sensitivity
132(1)
3.17 Conclusion
133(1)
List of acronyms
134(1)
References
135(4)
4 Innovative motors and shape optimisation 139(50)
Lidija Petkovska
Goga Cvetkovski
4.1 Trends in electric motor technology
140(2)
4.2 What makes an electric motor innovative?
142(16)
4.2.1 Novel design topologies
143(2)
4.2.2 Novel materials
145(8)
4.2.3 Novel production technologies
153(4)
4.2.4 Concluding remarks
157(1)
4.3 Design optimisation of electric motors
158(18)
4.3.1 Nature-inspired algorithms
159(5)
4.3.2 Case study
164(5)
4.3.3 Optimisation results
169(4)
4.3.4 Comparative analysis
173(2)
4.3.5 Concluding remarks
175(1)
4.4 FEA-based shape synthesis of electric motors
176(8)
4.4.1 Case study background
177(1)
4.4.2 Numerical experiment
178(1)
4.4.3 Selection of optimal shape design
179(1)
4.4.4 Analysis of the results
180(3)
4.4.5 Concluding remarks
183(1)
4.5 Summary
184(1)
References
185(4)
5 Frontiers and challenges of new ferromagnetic materials 189(28)
Barbara Slusarek
Marek Przybylski
Slawomir Wiak
5.1 Ferromagnetic materials
189(7)
5.1.1 Soft magnetic materials
190(3)
5.1.2 Semi-hard magnetic materials
193(1)
5.1.3 Hard magnetic materials
193(3)
5.2 Methods of magnetic materials production
196(3)
5.3 Application of ferromagnetic materials
199(6)
5.4 Frontiers of application of ferromagnetic materials
205(5)
5.4.1 Magnetic properties
205(1)
5.4.2 Mechanical properties
206(2)
5.4.3 Temperature properties
208(1)
5.4.4 Shape and dimension limitation
208(1)
5.4.5 Price limitations
209(1)
5.5 Challenges of new ferromagnetic materials
210(2)
5.5.1 Soft magnetic materials
210(1)
5.5.2 Hard magnetic materials
211(1)
5.6 New technologies of magnetic materials production
212(1)
5.7 Conclusion
212(1)
References
213(4)
6 Synthesising metamaterials with 3D printing and conductive layers 217(46)
Slawomir Hausman
Lukasz Jopek
6.1 3D gradient dielectric metamaterials
217(8)
6.1.1 3D gradient dielectric metamaterial anatomy
217(2)
6.1.2 3D metamaterial synthesis techniques
219(3)
6.1.3 Example application - flat Luneburg lens
222(3)
6.2 Resonators and resonator arrays
225(2)
6.2.1 Introduction
225(2)
6.3 Circuit-equivalent models of resonators
227(4)
6.4 Distributed circuit-equivalent models of resonators
231(4)
6.4.1 Resonator arrays - mutual coupling
232(3)
6.5 Full-wave FDTD numerical models of resonators
235(5)
6.6 Flat metasurface lens for MRI applications
240(4)
6.7 Artificial magnetic conductor
244(7)
6.7.1 Reflection phase estimation method
245(2)
6.7.2 AMC structure optimisation
247(4)
6.8 Frequency-selective metasurfaces in the THz band
251(2)
6.9 2D and 3D printing techniques
253(4)
6.9.1 3D printing complex internal structures
253(2)
6.9.2 Transforming 3D voxel images to G-code directly
255(2)
6.10 Conclusion
257(1)
References
258(5)
7 Industrial design perspectives 263(36)
Xose M. Lopez-Fernandez
7.1 Current and potential trends
264(6)
7.1.1 Design of complex geometries for manufacturability
265(1)
7.1.2 3D printing in manufacturing metamaterials
266(1)
7.1.3 Opportunities and future directions
267(2)
7.1.4 Disruptive design perspectives
269(1)
7.2 Applicability and adaptability
270(5)
7.2.1 Rapid prototype
271(1)
7.2.2 Industrial design challenges
271(3)
7.2.3 Limitations in integration AM with metamaterials
274(1)
7.2.4 Topology optimisation and additive manufacturing
274(1)
7.3 Research progress and industry application
275(4)
7.3.1 Manufacturing and prototyping
276(2)
7.3.2 AM integration challenges to scaled production
278(1)
7.3.3 Industrial sectors and applications
278(1)
7.4 Market segments
279(5)
7.4.1 An overview of current size and growth trends
280(3)
7.4.2 Online 3D printing and services providers
283(1)
7.5 Promising directions and examples
284(8)
7.5.1 Successful examples in industry
285(6)
7.5.2 How AM could innovate future electrical machines
291(1)
7.6 Conclusions
292(1)
References
293(6)
Conclusion 299(4)
In memoriam 303(2)
Index 305
Paolo Di Barba DSc, PhD, is a full professor of electrical engineering at the University of Pavia, Italy. His scientific interests focus on the analysis and synthesis of fields, with emphasis on evolutionary optimisation algorithms for solving inverse problems. In particular, he substantially contributed to importing the theory of Pareto optimality from microeconomics and developing it in computational electromagnetics. He is author of more than 200 papers, published in international journals, and three books.



Sawomir Wiak DSc, PhD, MEng, Multi DHC is a professor in and the director of the Institute of Mechatronics and Information Systems, ód University of Technology, Poland. His scientific interests focus on numerical methods, expert systems, e-learning systems, MEMS, mechatronic systems, and engineering software. He is the author or co-author of over 400 publications, including 19 monographs and textbooks, and member of 17 international steering committees and 11 advisory boards.