Additive Manufacturing with Novel Materials E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 835

Veröffentlichungsjahr: 2024

Beschreibung

ADDITIVE MANUFACTURING With NOVEL MATERIALS The book explores practically the latest advancements and techniques in 3D and 4D printing using innovative and unconventional materials. This book comprehensively provides insights into various additive manufacturing processes, novel materials, and their properties, as well as the basic knowledge of AM process parameters, post-processing techniques, and their applications. It also explores the fundamental concepts and recent advancements in the development of novel materials for several applications, with special emphasis on platforms like AM techniques for polymers, ceramics, metallic materials, composites, nanomaterials, hydrogels, etc. Specific topics like environmental aspects of 3D printing and advanced 4D printing are also introduced. The technological aspects of AM are discussed in a concise and understandable way, with extensive illustrations. Also covered are the challenges and opportunities that arise from 3D printing with these materials. Audience The book will benefit researchers and industry engineers who work in additive manufacturing, mechanical engineering, 3D/4D printing, and materials science.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Brief Glimpses of Additive Manufacturing Techniques

1.1 Introduction

1.2 Polymer-Based AM

1.3 Surgical Planning

1.4 Titanium Alloy

1.5 Thickness Control Using Machine Learning

1.6 Carbon Fiber-Based AM

1.7 Ceramics-Based AM

1.8 Wire Polymer-Based AM

1.9 Nanomaterial-Based AM

1.10 Direct Ink Writing (DIW)

1.11 Hull of Soy

1.12 Laser Powder Bed Fusion

1.13 Future Challenges

1.14 Future Scope of AM

1.15 Conclusion

References

2 Recent Developments in Additive Manufacturing Equipment’s and Its Processes

2.1 Introduction

2.2 Equipment and Procedures for Polymer Additive Manufacturing

2.3 Equipment and Procedures for Metal Additive Manufacturing

2.4 Equipment and Procedures for Ceramics Additive Manufacturing

2.5 FDM 3D Printing Technique

2.6 Hot Melt Extrusion Method—Polymers

2.7 Advancements in Additive Manufacturing

2.8 Conclusion

References

3 Computational Modeling of Additive Manufacturing—Overview, Principles, and Simulations in Different Scales

3.1 Introduction

3.2 Atomistic Simulation

3.3 Mesoscale Modeling

3.4 Macroscale Modeling

3.5 Machine Learning

3.6 Conclusion

References

4 Characterization Methodologies for Additive Manufacturing: From Feedstock to the Final Component

List of Abbreviations

4.1 Introduction

4.2 Characterization of Solid Feedstock Materials

4.3 Characterization of Liquid Form Feedstock

4.4 Characterization of Additively Manufactured Parts

4.5 Conclusion

References

5 Additive Manufacturing of Polymeric Materials: Process and Properties

5.1 Introduction

5.2 AM Process of Plastics and Their Properties

5.3 Conclusion

References

6 Additive Manufacturing of Metal-Matrix and Polymer-Matrix Composites

List of Abbreviations

6.1 Introduction

6.2 AM of Composite Materials

6.3 Effect of Parameters on Printed MMCs

6.4 Effect of Parameters on Printed PMCs

6.5 Applications of 3D-Printed Polymer Matrix and Metal Matrix Composites

6.6 Future Scope and Challenges

6.7 Summary and Conclusion

References

7 Postprocessing of Additively Manufactured Polymeric and Metallic Parts—A Review

7.1 Introduction

7.2 Defects Associated with Additively Manufactured Parts

7.3 Postprocessing Techniques for AM Processes

7.4 Conclusion

References

8 Additive Manufacturing of Nanoscale and Microscale Materials

8.1 Introduction

8.2 Microscale Additive Manufacturing

8.3 Properties of Materials

8.4 Nanocomposites Applications

8.5 Conclusion

References

9 Additive Manufacturing of Hydrogels: Process and Properties

9.1 Introduction

9.2 Application of 3D-Printed Hydrogels

9.3 Hydrogel Materials for Additive Manufacturing

9.4 Conclusion

References

10 Additive Manufacturing of Bulk Metallic Glasses

10.1 Introduction

10.2 Overview of Various Bulk Metallic Glasses Synthesized by Additive Manufacturing

10.3 Mechanical Properties of Bulk Metallic Glasses Synthesized Via Additive Manufacturing

10.4 Summary and Future Development Perspective

References

11 Additive Manufacturing of Tools and Dies for Metal Forming Applications

11.1 Introduction

11.2 Extrusion-Based Additive Manufacturing

11.3 Laser-Based Additive Manufacturing

11.4 Layer-Laminated Additive Manufacturing

11.5 Wire Arc Additive Manufacturing

11.6 Discussions

11.7 Challenges of Metals in Additive Manufacturing

11.8 Conclusion

References

12 Environmental Aspects of 3D Printing Metal and Alloys

12.1 Introduction

12.2 Additive Manufacturing Technologies

12.3 Metal and Alloy Materials

12.4 Positive Environmental Impact of Additive Manufacturing

12.5 Life Cycle Assessment

12.6 Sustainable Manufacturing

12.7 Negative Environmental Impact of Additive Manufacturing

12.8 Direct Sampling Devices

12.9 Health Damage Due to Exposure to Emissions

12.10 Safety Measures to be Followed During 3D Printing

12.11 Conclusion

Acknowledgment

References

13 Current Aspects of Additive Manufacturing in the Aerospace Industry

13.1 Introduction

13.2 AM Technologies for the Aerospace Industries

13.3 Summary of the Literature Study

13.4 Conclusion & Recommendation for Future Work

References

14 Four-Dimensional (4D) Microprinting: Materials, Processes, Challenges and Applications

14.1 Introduction

14.2 4D Printing Technologies

14.3 Micro 4D Printing: State of the Art

14.4 Smart Materials for Micro 4D Printing

14.5 Micro Additive Manufacturing Processes

14.6 Applications of Micro 4D-Printed Structures

14.7 Challenges and Future Outlook

14.8 Conclusions

Acknowledgments

References

15 Novel Technique to Measure Shape Memory Behavior of 4D Material

15.1 Introduction

15.2 Additive Manufacturing and Methodology

15.3 Analysis

15.4 Conclusions

References

16 Additive Manufacturing for Building and Constructions: Overview, Applications and Challenges

16.1 Introduction

16.2 Techniques of AM Used in B&C

16.3 Construction Materials for AM

16.4 Properties of the Printable Construction Materials

16.5 Applications of AM in B&C Industries

16.6 Challenges and Future Developments of AM in B&C Applications

16.7 Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Additive manufacturing patent count based on country, year, and IPC ...

Chapter 3

Table 3.1 Various aspects of sintering simulations from literature.

Table 3.2 Various aspects of crystallization simulations from literature.

Table 3.3 Various aspects of structural evolution simulations from literature....

Table 3.4 Various aspects of mixed type of atomistic simulations from literatu...

Table 3.5 Various aspects of simulations based on cellular automata.

Table 3.6 Various aspects of simulations based on Monte Carlo model.

Table 3.7 Various aspects of simulations based on finite element-coupled model...

Table 3.8 Various aspects of phase-field modeling from literature.

Table 3.9 Applications of mesoscale FEM in additive manufacturing.

Table 3.10 Applications of macroscale FEM in additive manufacturing.

Table 3.11 Review of ML-based studies in additive manufacturing.

Chapter 6

Table 6.1 Classification of AM processes.

Table 6.2 Materials used in different AM techniques [2].

Table 6.3 Research on MMCs fabricated using different AM processes.

Table 6.4 Research on the PMCs fabricated using different AM techniques.

Table 6.5 Applications of 3D-printed MMC and PMC products.

Chapter 7

Table 7.1 Additive manufacturing processes as per ASTM F42 [6].

Table 7.2 Brief summary of different 3D printing polymeric defects.

Table 7.3 Brief summary of different defects associated with 3D-printed metall...

Chapter 10

Table 10.1 Mechanical properties of different metal-based BMGs synthesized via...

Chapter 12

Table 12.1 Powder bed fusion technology and materials [10].

Table 12.2 Energy utilization during fabrication using various processes [17]....

Table 12.3 Potential hazards from different technologies used in 3D printers w...

Chapter 13

Table 13.1 FDM process parameters and their descriptions for application [43]....

Table 13.2. Additive nonmetal technologies in the aerospace industry additive ...

Chapter 14

Table 14.1 Various applications based on materials and structures from the lit...

List of Illustrations

Chapter 1

Figure 1.1 Polymer composite printing methods and applications [34].

Figure 1.2 Layer thickness controlled by machine learning [47].

Figure 1.3 Future challenges of AM [Redrawn from reference 59].

Chapter 2

Figure 2.1 Various advancements in additive manufacturing process [25].

Figure 2.2 Selective laser melting [40].

Figure 2.3 Electron beam melting [41].

Figure 2.4 Schematic of laser engineered net shaping process [47].

Figure 2.5 Additive manufacturing — binder jetting [49].

Figure 2.6 Ultrasonic additive manufacturing process [54].

Figure 2.7 Hot melt extrusion process [64].

Figure 2.8 Parameters influencing the 4D printing process [76].

Chapter 3

Figure 3.1 Schematic of process flow in molecular dynamics.

Figure 3.2 Some of the postprocessed results from rapid cooling simulation of Ni...

Figure 3.3 Concept of sharp and diffuse interface using order parameter.

Figure 3.4 Various morphologies generated. (a) Grain growth morphology. (b) Voro...

Figure 3.5 Stress tensor components. (a) σ11. (b) τ12. (c) τ13. (d) σ22. (e) τ23...

Figure 3.6 Schematic of process flow in thermomechanical analysis.

Figure 3.7 Results of AM simulation. (a) Effective stress; (b) Displacement; (c)...

Chapter 4

Figure 4.1 Hall flowmeter funnel method apparatus.

Figure 4.2 Apparent densities of different powders measured by Hall flowmeter fu...

Figure 4.3 Carney funnel method apparatus.

Figure 4.4 Scott volumeter method apparatus.

Figure 4.5 Arnold meter method apparatus.

Figure 4.6 Tap density method apparatus.

Figure 4.7 Tap densities of various powders.

Figure 4.8 Hall flowmeter funnel method apparatus.

Figure 4.9 Flowrates of various powders using the Hall flowmeter funnel method. ...

Figure 4.10 Carney funnel method apparatus.

Figure 4.11 Flowrates of various powders using Carney funnel method.

Figure 4.12 Tensile properties of Ti-6Al-4V fabricated by EBAM and LBDED methods...

Figure 4.13 Hardness of two materials using the Vickers hardness method.

Figure 4.14 Hardness of two materials using the Rockwell hardness method.

Figure 4.15 Impact strengths of two materials using the ASTM E23 method.

Figure 4.16 Pin-on-disc wear method apparatus.

Figure 4.17 Corrosion resistance of (a) 316L SS and (b) AlCoCrFeNi.

Chapter 5

Figure 5.1 Printing of polymeric materials [117].

Figure 5.2 SLA [118].

Figure 5.3 FDM [119].

Figure 5.4 AM process of plastics.

Figure 5.5 Conductive ABS printed samples [49].

Figure 5.6 Schematic reparation of preparation of filament and testing [64].

Figure 5.7 Polymers in versatile AM implications.

Chapter 6

Figure 6.1 Steps involved in 3D printing.

Figure 6.2 AM techniques.

Figure 6.3 Classification of AM materials.

Figure 6.4 Effect of the laser power density on density of printed MMCs at scan ...

Figure 6.5 Effect of scanning speed on microhardness [13].

Figure 6.6 Effect of scan speed on density [74].

Figure 6.7 Effect of temperature on density [23].

Figure 6.8 Tensile properties of CF/PA6 composites with the three different depo...

Figure 6.9 Effect of printing speed on the electrical conductivity [89].

Figure 6.10 (a) Stress-strain diagram of tensile specimens with parallel and 45°...

Figure 6.11 Applications of AM in different industries.

Chapter 7

Figure 7.1 Heat treatment as postprocessing technique for 3D-printed polymeric p...

Figure 7.2 Schematic of ultrasound treatment process [47].

Figure 7.3 (a) Sa and Sb; (b) Glossiness as a function of CMP one step polishing...

Figure 7.4 Schematic of laser shock peening technique.

Figure 7.5 Electron backscattering diffraction (EBSD) maps of SLM-fabricated Ti6...

Figure 7.6 Schematic of laser polishing technique.

Figure 7.7 Physical illustration of LP samples [67].

Figure 7.8 SEM images of 316L SLM specimen surface showing unprocessed surface o...

Figure 7.9 Laser microscopic image of SLM surfaces polished under 500 W for 20 m...

Figure 7.10 Plot between average surface roughness and % of abrasive concentrati...

Figure 7.11 Microhardness map for thermal postprocessing of AlSi10Mg samples cro...

Chapter 8

Figure 8.1 Micro-structured surfaces by additive manufacturing [8].

Figure 8.2 (a) % Elongation and toughness data (b) Stress-strain plots, for tens...

Figure 8.3 (a) Schematic of electrohydrodynamic redox (EHD-RP) 3D printing (b) S...

Figure 8.4 SEM images of the fabricated 100%-PEGDA microwells and microarchitect...

Chapter 9

Figure 9.1 (A1, A2) Cylindrical shaped hydrogel, (B1, B2) sheet structured hydro...

Figure 9.2 Storage and loss modulus curve for MET- and LFH-based smart hydrogel ...

Chapter 10

Figure 10.1 Property comparison of metallic glass and other materials. Reproduce...

Figure 10.2 Statistics of development in metallic glasses via AM route [9].

Figure 10.3 Schematic showing the principle of selective laser melting technique...

Figure 10.4 (a–d) Different shapes of Zr-based BMG samples synthesized by SLM. M...

Figure 10.5 (a–c) Different shapes of Fe-based glassy parts synthesized by SLM p...

Figure 10.6 Schematic showing the overall process of laser engineered net shapin...

Figure 10.7 (a) Top view and (b) side view of Zr55Cu30Al10Ni5 glassy specimen sy...

Figure 10.8 Schematic images of (a) LFP system and (b) various process steps. Mo...

Figure 10.9 (a–d) Various BMG parts fabricated by LFP with different geometries....

Figure 10.10 Compressive engineering stress-strain curves of the selective laser...

Chapter 11

Figure 11.1 Key factors in fabricating the tools and die for metal forming.

Figure 11.2 Functional requirements of tooling.

Figure 11.3 Fabrication methods of tool and die.

Figure 11.4 (a) Die made by FDM and (b) finished component [4].

Figure 11.5 Polylactic acid tools manufactured by 3D printer [5].

Figure 11.6 V-bending process for A1100-H14 sheet with plastic tools [5].

Figure 11.7 Schematic diagram of EAM [8].

Figure 11.8 CAD model of the Mold.

Figure 11.9 Deformed shapes of samples [10].

Figure 11.10 (a) Punch and die manufactured by using 3D printing: (b) workpiece ...

Figure 11.11 Digital enabled manufacturing processes.

Figure 11.12 Inverted flow channel design for AM die manufacturing [14].

Figure 11.13 Sample made by SLM process [20].

Figure 11.14 Assemble of die made by AM [24].

Figure 11.15 Schematic description of the hybrid A/SM process [25].

Figure 11.16 Schematic diagram of new type WAAM with forging (a) HF-WAAM inputs,...

Figure 11.17 (a) Geometry of a forged sample; (b) shape of an AM deposited sampl...

Figure 11.18 The process begins with basic forged geometry and progresses to AM ...

Chapter 12

Figure 12.1 Flow chart for manufacturing objects by 3D printing [2].

Figure 12.2 The material and energy flow in additive manufacturing [10].

Figure 12.3 Carbon dioxide emission during various manufacturing processes [16]....

Figure 12.4 (a) Life cycle of a machine tool conceptual view, (b) product recove...

Figure 12.5 Impact categories during life cycle assessment [34].

Figure 12.6 Carbon tracer for groundwater studies [36].

Figure 12.7 Condensation particle counter setup [46].

Figure 12.8 (a) Tapered element oscillating microbalance (TEOM) and (b) TEOM app...

Figure 12.9 Nanoparticle surface area monitoring [51].

Figure 12.10 Health risks of exposure to heavy metal ions [58].

Figure 12.11 Skin penetration of nanoparticles [59].

Figure 12.12 Oxidative stress damage the skin due to exposure to metal particles...

Figure 12.13 Regional particle deposition in the lungs based on the particle dia...

Figure 12.14 Surface oxidation phenomenon of stainless steel under laser irradia...

Figure 12.15 Diagrammatical representation and bioaccumulation of lead-heavy met...

Figure 12.16 Metal ions in Alzheimer’s disease [69].

Figure 12.17 Hearing loss risk and DNA methylation due to lead and cadmium expos...

Figure 12.18 Human exposure to metals in fused filament fabrication 3D printing ...

Figure 12.19 (a) Emissions and chemical exposure potential from stereolithograph...

Chapter 13

Figure 13.1 Classification of AM technology for aerospace applications.

Figure 13.2 Metal and nonmetal AM technologies for aerospace applications with e...

Figure 13.3 Schematic representation of SLA process.

Figure 13.4 Schematic representation of electron beam melting (EBM) process.

Figure 13.5 Schematic representation of fused deposition modeling (FDM).

Figure 13.6 Working schematic of selective laser sintering.

Figure 13.7 Working schematic of laminated object manufacturing.

Figure 13.8 Schematic of additive friction stir deposition [49].

Chapter 14

Figure 14.1 Various types of 4D printing.

Figure 14.2 Various AM processes involved in 4D microprinting.

Figure 14.3 Various µSLA setups (a) scanning type µSLA and (b) projection type µ...

Figure 14.4 Schematic illustration of the two-photon polymerization setup [59]. ...

Figure 14.5 Process of laser transfer micro AM.

Figure 14.6 Schematics showing: (a) a continuous inkjet printer, (b) an on-deman...

Figure 14.7 Micro laser sintering system: 1-secondary focusing lens/optics, 2-be...

Figure 14.8 Beam deposition AM: (a) focused electron/ion beam-induced deposition...

Figure 14.9 Structural integrity test of microswimmer. Reprinted with permission...

Figure 14.10 Functional test of microswimmer with magnetic actuation. (a) Initia...

Chapter 15

Figure 15.1 Representation of the technique to measure shape memory parameters. ...

Figure 15.2 Illustration of RE model and CAD model.

Figure 15.3 Flow of the measurement technique.

Figure 15.4 Generation of paraboloid surfaces under bi-axial stresses.

Figure 15.5 Shape recovery of rectangular plates with respect to thickness for θ...

Figure 15.6 Surface comparison between configurations of shape memory cycle.

Figure 15.7 Comparison between paraboloid surfaces in recovery stage.

Figure 15.8 Distribution of deviation at nodes in recovery stage with respect to...

Chapter 16

Figure 16.1 (a) Illustration of binder jetting concept, (b) insight view of mate...

Figure 16.2 (a) Design overview of the multi-nozzle extrusion print head (b) ons...

Figure 16.3 Types of 3D printable materials in B&C. Reproduced from Ref [49]. Co...

Figure 16.4 Overview of geopolymer production. Reproduced from Ref [67]. Copyrig...

Figure 16.5 Influences of print speed and material flow on printability. Reprodu...

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

Pages

ii

iii

iv

xvii

xviii

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Additive Manufacturing with Novel Materials

Processes, Properties and Applications

Edited by

R. Rajasekar

Department of Mechanical Engineering, Kongu Engineering College, Tamil Nadu, India

C. Moganapriya

Department of Mining Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

and

P. Sathish Kumar

The Sirindhorn Thai-German Graduate School of Engineering, King Mongkut’s University of Technology, Bangkok, Thailand

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-19791-0

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

It is well-known that technology is rapidly moving towards additive manufacturing (AM) for the design and manufacture of high-performance materials for aerospace, medical, energy, automotive applications, and so on. AM is being utilized everywhere in the manufacturing process of parts to avoid difficulties and produce parts with high quality at a high rate. It is expected that the AM processes can bring another industrial revolution. AM is becoming more and more popular for producing components with intricate geometries, customizing products, and generating small quantities and prototypes.

This book provides insights into various AM processes, novel materials and their properties, as well as basic knowledge of AM techniques and their applications. It also explores the fundamental concepts and recent advancements in the development of novel materials for several applications, with special emphasis on platforms like AM techniques for polymers, metallic materials, composites, nanomaterials, hydrogels, etc. Special topics like environmental aspects of 3D printing and advanced 4D printing are also introduced. The technological aspects of AM are discussed in a very clear and understandable way with the help of self-illustrating artworks.

In this context, the book provides insight for all researchers, academicians, post-graduate or senior undergraduate students, and industry professionals working in this important area. We thank all the authors for their valuable research and input, and we offer our sincere thanks to the Scrivener and Wiley publishing teams for their help with this book.

R. Rajasekar

C. Moganapriya

P. Sathish Kumar

November 2023

1Brief Glimpses of Additive Manufacturing Techniques

V. Bhuvaneswari

Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

Abstract

Manufacturing has undergone various changes since the beginning of wheel manufacturing in the stone age. Subtractive manufacturing was predominantly used for many years. Additive manufacturing started in the 1980s, moving manufacturing towards a different path, which led to freedom to produce sophisticated components very easily. This chapter focuses on the current trends in 3D printing techniques along with envisaging its scope through the patent landscape. At the end of this chapter, the researcher gets a clear picture of the current state of additive manufacturing techniques and the future directions in this field.

Keywords: Additive manufacturing, 3D printing, polymer, metal, ceramics

1.1 Introduction

Layer-by-layer fabrication, which has been used to create 3D objects since the 1980s [1, 2], paves the path for intricate structures through carefully tailored characteristics and functions. Rapid prototyping was the name given to this novel technique of producing physical prototypes. Raw materials could either be solid, liquid, or powder in this developing technology [3]. Additive manufacturing (AM) processes have found broad use in industry and medicine because of their versatility in producing complex and precisely crafted items with preferentially constructed characteristics [4]. As a result, AM techniques have accelerated product growth by less expensive, shorter manual interaction and product cycle, and fewer constraints. Binder jetting (BJ), direct ink writing, sheet lamination, and digital light processing are just some of the many techniques and processes that have advanced in AM processes [5]. The collection of AM methods is often referred to as “3D printing (3DP).” Rather than being confined to the creation of static, single-function 3D objects, 3DP techniques can be expanded to include the creation of multifunctional, ever-changing shapes for the duration of an object’s lifespan. Four-dimensional printing (4DP) allows for greater customization and variety in the manufacturing process. The same methods as AM are used in 4DP, with the addition of a gradual alteration to the material’s properties [6]. Time is considered a fourth dimension, with smart materials having the ability to adapt to changing conditions over time. As a result, these substances can be used in a variety of biomedical contexts, including sensors, robots, complex components, scaffold implants, and several other uses [7–10].

Computer-aided design (CAD) modeling software is used to define the shape and structure of smart materials with the desired characteristics and the material is then sliced into layers of the appropriate thickness for 3DP or 4DP. Using external stimuli to modify the material’s specific item and, by extension, the product’s shape, would be the next step in the evolution of products made from some other sort of smart materials. The pH, temperature, electromagnetic field, light, moisture, and/or moisture content are all examples of these kinds of externally applied stimuli [11–13]. Whether it is a phase shift, stress relaxation, deformation, or anything else, these factors have the potential to alter the material’s chemical and physical properties. Materials are further defined by their mechanical properties, which include their response to external stimuli in terms of flexing, inflammation, contortion, and size reduction [14, 15]. There must be extensive research into the development of smart materials in order to produce a product with the desired qualities for a given set of applications. Concerns remain about the ability to fabricate new resources using AM procedure and to trigger their characteristics using exterior factors.

Several smart materials fabricated using various AM techniques are described in the literature [16–22], each with their own set of applications and associated constraints like toxic effects, degradation rate, bioactivity, and so on. However, research into remotely triggered materials, such as polymers nanocomposites, is lacking in the existing literature. Functional materials and novel designs, such as cardio stents [23] and porous bone used in the biological field [24], are essential for smart systems and devices. In this paper, we present the results of a systematic analysis focused on the glimpse into3D printing and the identification of polymeric composites that can be activated by outside factors and have the tailored architecture necessary for use in various fields of application. This paper provides a comprehensive examination of smart nanocomposites constructed from functional polymers, focusing on their design and the most appropriate AM method for their various applications in the medical field. The paper begins with a brief introduction to various AM approaches that are predicated on the fed material, then moves on to discuss operational polymeric material with varying stimuli-reaction behaviors and distinctive design, before finally wrapping up with potential future applications and fields of study. This comprehensive overview will offer the investigators information on the most recent polymeric nanocomposite materials and their potential applications. Additive manufacturing is a novel technique that utilizes modeling software and CAD files to create real-world 3D objects at full scale [25]. Time and money spent on manufacturing have been drastically reduced due to its use [26]. This is especially true for products with multiple materials and intricate shapes. In contrast to traditional methods, 3D manufacturing allows for the fabrication of complex designs by means of the accurate implantation of thin layers, thus, eliminating the need for assembly protocol and resulting in a lightweight, one-piece geometry. As a result, it has simplified issues associated with stocking and fabricating components. Although traditional methods are gradually being phased out for mass production, they are still occasionally considered for use on a smaller scale [27]. Because of its benefits in targeted therapy and as a suitable therapeutic, AM has gained widespread acceptance in a number of biomedical applications [28, 29], despite the fact that 3D printing has one major drawback: a slow printing rate. New solutions inspired by robust needs have been made possible by the development of AM methods. Polymers and their nanocomposites, for example, have led to the development of a number of distinct protocols [30–33]. This chapter evaluates some of the most recent and cutting-edge resources.

1.2 Polymer-Based AM

As a result of the synergistic combination of regular polymers and the electrochemical characteristics of metals or semiconductors, research into electrical conductor polymer composites has attracted significant attention. The increased design freedom, more complex shapes, and faster manufacturing times made possible by AM are all reasons for optimism in the field of conductive polymer composites. Materials can be 3D printed using a variety of AM techniques, such as injection molding, vat polymerization, material jetting, powder bed fusion, and laminated object manufacturing. In this chapter, we provide a glimpse into recent developments in the sector of conductive polymeric materials created for AM, speed up the configuration and advancement of 3D printable electrical devices. A close look is taken at the advantages and disadvantages of several AM techniques, resource necessity and recent developments in 3D printing of conductive polymeric materials are discussed, and some of the most exciting electronic applications, including wearable electronics, power storage, power electronics, and more are highlighted (Figure 1.1) [34]. Recent advances in 3DP technologies and potential uses for electrically powered CPCs are discussed. 3D printing processes that can be used to produce these materials are also presented. In this chapter, a deep dive into the world of conductive materials for 3D printing is taken. This motivates the discussion of few design constraints and dynamic applications of 3DP in bio-electronics. There has been a lot of work done over the past few years to develop this new field of study. Still, it has a way to go before reaching its full potential, as several obstacles must be overcome. When creating CPCs for AM, a number of factors must be taken into account. There is a need for so many polymeric systems which can be utilized in AM processes and have high electrical conductivity. The small number of CPCs currently amenable to 3DP may be a result of the challenge of integrating semiconducting substances within polymer matrices. This necessitates a synergistic combination of materials to improve dispersibility while maintaining high electrical conductivity. The use of functional fillers in 3DP of CPCs has emerged as an exciting zone with numerous potential uses. The relationship among fabrication techniques, framework, and the final appearance of CPCs for AM needs to be studied further. This allows for a more nuanced investigation into the effects of the printing method and environmental factors on the electrochemical characteristics of printed specimen fabricated from various conductive specimens. Given that the effectiveness of a printable product may be affected by factors that differ across AM processes, it is important to develop electronic test procedures that are method specific. Despite its many advantages over traditional manufacturing methods, AM is limited by factors like minimal print velocities, pixel size, and indirect control over the crystal structure and features of the finished item. Therefore, it is critical to create cutting-edge 3DP methods suitable for CPCs of varying densities and characteristics (light transmission) without compromising overall printing speed. Improvements in printing resolution necessitate a closer look at matrices and fillers and the matching of their properties. Since only some parts of a 3D object need to be processed at a high resolution, research into 3DP methods that permit adaptable alteration of printing resolution is warranted such that large volume generation rates can be achieved. The evolution of printing technology and the introduction of new materials have enabled it to print structures out of responsive components, which can change their shape or properties over time. By invoking a transformation in form or properties in 3D-printed objects, a temporal dimension into the printing process is introduced and paves the way for the advent of 4DP. Changes in environmental conditions (heat, illumination, magnetic forces, electrical pulses, etc.) can cause shape memory resources to return to their natural programmed dimensions [35–40]. The added flexibility and adaptability are a result of these features. Recent years have seen extensive research into developing semiconducting composite materials for 4DP of shape patterns [41, 42]. For the purpose of creating temperature-sensitive electrical devices for use in atmospheric regulation, Chan et al. [43] used post-printing electroplating conductive coating method to instil a high conductivity Cu outer skin on 3D-printed shape memory composites. The use of such adaptable structures could improve the performance of new sensors, portable electronic devices, design field of robotics, and non-invasive medical equipment.

Figure 1.1 Polymer composite printing methods and applications [34].

It is also important to think about things like digital file encryption and the security of the printing process. The steps involved in AM are complex and varied. If the CAD folders are altered, the safety of 3D-printed functional parts can be compromised, including components of critical safety mechanisms in automotive and aerospace systems. Due to these factors, AM processes are an enticing objective for security-based attacks, so protecting the design configuration document from corruption is essential. Yampolskiy et al. [44]’s research offers an additional thorough initiation and evaluation. More and more people have access to 3D printers and the raw resources require to create their own prints as 3D printing systems become more widely available. One viable option is to host 3D digital printing designs of commonly used components on shared digital repositories; this would allow users to access the files, print the 3D goods, and fix any flaws in the model using their own printing equipment.

1.3 Surgical Planning

Recent decades have seen a meteoric rise in medical innovation, driven largely by the introduction of cutting-edge surgical techniques and their accompanying high-tech equipment. This has helped doctors and surgeons pinpoint the onset of a disease, track its progression over time, prepare for surgery, perform surgery, and develop postoperative protocols. Technological developments in minimally invasive surgery, virtual reality, and robotics have allowed surgeons to better plan for the preoperative, intraoperative, and postoperative phases of a procedure even when they have only a limited field of vision. Recently, surgeons have been able to develop and formulate cutting-edge pre- and post-operative planning thanks to advancements in AM. By using AM, a physical representation of a body part can be created in three dimensions, which can help surgeons visualize, comprehend, and evaluate the part’s physical condition before actually operating on it. Thus, these models are employed in pre-operative preparation for actual surgical procedures. Postoperative rehabilitation is another area where AM has proven useful. The structure and dimensions of the patient’s body part can be measured with the help of a 3D-printed model prototype. The conclusion of the operation and any potential changes that may occur after can be evaluated more precisely with the help of this data. Medical fields that can benefit from 3D-printed prototypes and prosthetics after surgery include orthopedics, ceramic and polymer blends scaffolds for implants, ocular prostheses, and crystal images for facial deformities, to name a few. In addition to requiring less time to produce than their conventional counterparts, these devices can also provide postoperative patients with ergonomic comfort and support. It goes without saying that AM-made prototypes are essential for providing efficient assistance to the health industry in the event of both pre- and post-surgical strategic plan in aspects of improved precision and repetitions for simulated surgical procedure, reduced surgical duration, accelerated fabrication time, enhanced product quality, and a wide variety of individualized product options [45].

1.4 Titanium Alloy

Applying a pulsed magnetic field (PMF) to a metal during its solidification process has been shown to reduce grain size and boost the material’s mechanical properties. Laser additive manufacturing (LAM) has shown promise, but fewer works have been reported in this area, and the method of grain growth following the PMF is still unknown. To investigate the thermal-fluid characteristics of the Ti-alloy melt pool produced by the laser scanning process under the influence of a merged direct current (DC) electric field and PMF, numerical models were developed in the present work. Microstructure change after LAM was explained by discussing the temperature distribution, melt pool evolution, and magneto-oscillation impact in the weld zone. According to the findings, electric-magnetic fields applied during the LAM process can significantly alter the melt pool’s temperature distribution, shape, and dimensions, as well as the melt flow characteristics. The augmented fluid-fluid motion and constant stimulus force in the melt, as well as the rising temperature slope, crystallisation rate of growth, and cooling rate at the liquid-solid interface, are all likely to play a role in producing the desired sophisticated crystal structure [46].

1.5 Thickness Control Using Machine Learning

To get high-resolution printing in a variety of materials, non-contact AM via aerosol jet printing (AJP) is a hopeful new option. Thinness control is a major issue that has prevented AJP from gaining wider acceptance in the materials science community. In this chapter, we propose, choose, and measure the optimum model to deliberate and optimize the AJP production procedure by using a model-based design of experiments (MBDoE) structure which incorporates physics-informed designs, regression models, and information criteria. Four applicant physics-informed designs are proposed and trained using data that is derived from commissioning of system (for example, previous single variable sensitivity experiment). To verify these forecasting analytics with quantitative ambiguity, MBDoE finds a single extra ideal experiment, utilized to find the optimal experimental status to regulate the width of the printed film. Nonparametric Gaussian process regression (GPR) model, which does not include original data, is applied to the same dataset as a comparison benchmark. Our results show that the nonlinear physics-informed parametric model can be calibrated by just five experiments using MBDoE principles, and that it can outperform the black-box machine learning GPR model by means of this small amount of data. This important finding exemplifies a current pattern in data science: using physical information in predictive models can often greatly reduce the amount of data needed. As a result of implementing MBDoE, data efficiency was improved even further. The suggested data science structure is generic by design, making it amenable to adaptation for use with systems other than AJP that employ exploratory and AM techniques [47].

For the first time, advanced tools like model-based design of experiments (MBDoE) are used to enable fine-grained regulation of printed film thickness in AJP procedures. Figure 1.2 shows the schematic of layer thickness controlled by ML algorithms. As a starting point, a dataset with 22 experiments is used from the commissioning of AJP apparatus and other projects that is typically ignored. These preliminary data are used to propose a library of eight applicant physics-informed models, which are then narrowed down to four models using Akaike Information Criteria (AIC). To enhance the reliability of these models’ predictions, MBDoE zeroes in on a single experiment that will yield the best results. Four more experiments are carried out, and the results confirm that MBDoE-informed experimental conditions are much more insightful than those chosen predicated purely on specialist instinct. Finally, the cross-validated best model is used to devise strategies for adjusting the power output, carrier fuel water flow rate, layer water flow rate, and scan speed to attain the preferred layer depth with negligible prototype estimation uncertainty. Additionally, with the help of Fisher information matrix for retroactive analysis, we find five optimal investigations are required to treat the recognized physics-informed multiple linear regression model. Based on that, it appears that, a small number of experiments is required to adapt the planned work order for use with AJP in another substance scheme. The method’s efficacy is measured against that of a black box nonparametric model trained on the repeated information. Researchers observed that the GPR model is most effective in areas with a large amount of available data. On the contrary, the nonlinear parametric model that is grounded in physical principles performs better than GPR in sparsely sampled regions. When looking at just the five best experiments in the dataset, this trend becomes especially apparent. In light of current trends in the ML literature informed by physics, these findings are not surprising. To reduce the amount of data needed for training, mathematical models should integrate science and experiential information whenever possible. This chapter provides a second example to back up this claim. In the future, we hope to apply the established correlation between output power, carrier gas water flow rate, circulation in the sheath, swiftness in printing, and depth to other AJP material systems for thermoacoustic metals and gadgets, such as AgSe [48] or BiSbTe [49]. Between five and ten optimum tests may be required for initial modeling when making changes to new material systems, with two important adjustments: (1) the limits of factors must be up to date to take into account shifts inside the toner viscosity, and (2) the linear regression is likely to differ for every scheme. The improvement of the model’s accuracy close to the operational bounds is another area worthy of further study. If the most promising model is taken as a starting point, it can be seen, for instance, that the linear and quadratic assumptions might not seize close to the functioning limits. Scientists speculate that slight model-form error accounts for the improved presentation of black-box GPR when dealing with a large amount of data. A hybrid model [50, 51] could be utilized to account for this minor logical error in thickness predictions in the future. In addition, the ink composition for AJP and other AM processes can be optimized with the help of the recognized physics-informed designs and the suggested data scientist’s structure.

Figure 1.2 Layer thickness controlled by machine learning [47].

1.6 Carbon Fiber-Based AM

Today, AM is the backbone of the engineering industry’s ability to discover and develop new and useful materials and tools. However, little task was done in the resource modeling work of each stage, and numerous experiments have demonstrated inconsistent results and emphasised various deficiencies in the structure, calling into question the implementation of the AM technique in real engineering applications. A micromechanical analysis using the sample quantity aspect has been considered, to take into account the consequences of the accumulated defects, and a state-field mathematical model was used as a prototype to show the impact of inter-fiber crack propagation. While satisfying geometric and material periodicity, MLA were used to acquire fiber dimensions and content for use in creating 3D models of reflective control volumes in the Abaqus environment. Furthermore, in both the cross line and in-plane shear test cases, periodic limit requirements were assumed for all representative volume elements (RVE). The model essentials and description of Abaqus were used in the analysis rather than a custom-built UDE because the analysis was performed using an open-source UMAT subprogram that related the phase-field equilibrium equations to the built-in heat transfer equation. Experimental outcomes for circumferential tension and compression for the filled carbon composites were used to calibrate the interface properties, and three main quantity component dimensions were used to verify the model. Results of the experiment and calculated results procured to use a phase-field fracture method were found to be consistent with one another for the circumferential tension and compression and shear actions of synergistically fabricated continuous-fiber-reinforced composites [52]. The remainder showed reliance on the type of reflective bulk aspect for certain pressure cases. Here, we present the arithmetic operations of the micromechanical behaviour of AM CFRP materials in tensile stress and in-plane shear, grounded in infinitesimal assessment and the macroscopic response are presented. The methods used in producing materials and getting them ready for microscopic inspections are covered, along with the benefits and drawbacks of this approach. Many of the advantages of CT over microscopy are presented, and the RVE design, which is derived from simple geometric hypotheses and assisted by the micro structural metrics is also discussed. Because of its support for the built-in features of Abaqus, the micro structural analysis presented here allows for the progress and assessment of recurrent RVEs with the help of the phase-field rupture hypothesis. It is possible because of the analogy proposed among phase-field rupture, the heat transfer differential calculations. This method made it easier to visualize the acquired result and to divide the domains depending on the different resources and functionality. In addition, differences in AM composite materials can be traced back to the deposit’s contact zones, as evidenced by both data dispersion and DIC images. The proposed RVEs were too small to accommodate the magnitude of these deficiencies, so they were integrated into the system alongside the fiber/matrix impact at the interface of the constituents. The limitations of this method were highlighted, and a multiscale strategy utilizing interface properties obtained experimentally was proposed for future research. Overall, the results of this study confirmed that the transverse tensile behavior of AM CFRP composites is consistent with the numerical simulations that use the phase-field fracture approach. When compared to the experimental scatter, the mathematical outcomes were consistent regardless of the RVE used. However, when comparing the in-plane shear behavior of the tested materials, RVE-1 and RVE-2 showed discrepancies with both experimental results and outcomes procured for the RVE-3, which leads to underestimating the shear strength and modulus. In the case of in-plane shear, however, RVE-3 overestimated the mechanical properties. Although this phase-field modeling approach was shown to characterize the transverse micromechanical behavior well, it was also shown that in-plane shear cases do not obey the exploratory outcomes after 1% of shear strain. Further, the model predicts a complete specimen failure even after surpassing the shear strength measured based on ASTM instructions, which does not occur in the experiments. Therefore, future research should take into account a variety of methods to experimentally identify and design shear behavior in AM-fiber-reinforced composites [52].

1.7 Ceramics-Based AM

Complex ceramic materials that emit light and scintillate using stereolithography 3DP are of interest for use as specialized phosphors and detectors. It is important to investigate the potential impact of the powders’ luminescent characteristics on the printing process because the starting substances for that kind of ceramic materials may acquire UV absorption bands. Complex garnet oxides, such as Y3Al5O12 and Gd3Al2Ga3O12, are discussed in this chapter. These compounds are well-known hosts for luminescent components. Slurries made from luminescent powders made via different chemical routes have their photopolymerization rates and printable regimes investigated. Compared to non-doped slurries, those containing Ce-doped powders that have a broad UV absorption band have much slower photopolymerization rates, and a high Ce doping virtually prevents being able to print with layers thicker than 25-50 m. In addition, it demonstrated how printing is affected by the powder synthesis method of choice. Layer thicknesses between 25 and 100 m are printable using slurries containing Tb-doped powder because of the powder’s high photopolymerization activity and similarity to that of the undoped powder. Using digital light processing 3DP, Gadolinium Aluminum Gallium Garnet (GAGG) was used to make net-like objects: wall thicknesses of 200-400 m and round holes of 200-700 m are examples of the sub millimeter features that can be achieved with Ce and GAGG:Tb ceramic. The luminescent properties of the starting powders, and more specifically the interconnection of the photoluminescence lattice vibrations band(s) with the 3D printer’s source of light emission, were shown to be a big negative in curing dynamics, thereby limiting the printing dictators that can be used. For this reason, at equivalent dopant concentrations, thicker layers could be printed using Yttrium Aluminium Garnet: Cerium (YAG:Ce) than GAGG:Ce. The photoluminescence vibrational band density and the required illumination dose for printing a given layer thickness were both lower for powder particles acquired by uniform precipitate than for powders synthesised by co-precipitation. If required to print luminescent ceramics with a DLP printer, options are limited unless you either use powders with undeveloped luminescent properties or select a printer whose light source does not overlap with the powder’s absorption bands [53].

1.8 Wire Polymer-Based AM

Fused filament fabrication (FFF) was used to 3D print a CWPC thermal gradient sensor, with the integrated wires acting as impedance detecting components. Thermoset polyurethane (TPU) and Cu wire, polylactic acid (PLA) and Ni wire, and PLA and Cu wire were all investigated as potential sensor combinations. Samples were 3D printed with varying numbers of layers for each composition to examine how sensor thickness affected performance. Distances between elements, electrical conductivity of polymers, and temperature coefficients of resistance of wires were measured. The 3D-printed CWPC performed admirably as a heat flux sensor in performance tests, proving its viability as a heat flux sensor in low temperature and low heat flux applications. Errors in measurements were below 10% for 2-layer specimens at the high thermal fluxes and below 17% for all other configurations. To accurately estimate heat loss from an insulated system, a 3D-printed flexible CWPC thermal gradient sensor was used in a study. Given its adaptability to a wide variety of polymers and sensing elements, the approach to sensor fabrication that is described here has broad potential for use. Gradient-type heat flux sensors with integrated wires that acted as impedance heat sensors were 3D-printed using continuous wire polymer composites (CWPC). Two-, three-, and four-layer structures containing the combinations PLA + Cu, PLA + Ni, and TPU + Cu were created. The measurement of heat flux was characterized quantitatively with respect to key design parameters that govern performance and accuracy. The sensors were tested by having an external heat flux superimposed and the two values compared. There was a good deal of concordance between the two methods, with the calculated heat flux within the margin of experimental error. For all heat fluxes, the sensors made of PLA + Cu and TPU + Cu had error rates of less than 17%, while those made of PLA + Ni had error rates of less than 12%. Uncertainty analysis revealed that a thicker sensor reduced the margin of error for the evaluated thermal gradient, however, a higher resistance is expended. When compared to other wires, the Ni wire used in the sensor was more sensitive because of its higher resistance. In general, the sensors functioned adequately for low-temperature, low-heat-flux applications, and their main benefit is that they can be modified to fit a specific geometric setting. To test the feasibility of measuring heat loss from an insulated pipe, a flexible TPU + Cu detector was manufactured and wrapped around a shielded heated rod. Good agreement was found between this study’s predictions and the conceptual thermal failure [54].

1.9 Nanomaterial-Based AM

There is a growing trend of combining the relatively new technologies of 3DP and nanotech to make all sorts of exciting and reasonably priced new items. In fact, 3DP technology has quickly come to dominate a number of industries and the medical field due to its remarkable advantages that were either unavailable before or prohibitively expensive when compared to other production techniques in both fields. As an industry, 3DP is built on the shoulders of two pillars: the advanced resources used in the fabrication, and the technical details required for printing them. Since 3DP materials can be put to use in so many different contexts, their development is crucial to the growth of the industry as a whole. This chapter takes a look at the many different nanomaterials that can be used in 3DP, as well as their advantages and how they affect the properties of the final products. The chapter also discusses the gaps in risk assessment related to 3DP and nanotechnology, which are necessary given the need to evaluate idea at the nano-scale to confront any possible hazards to both people and the environment. Furthermore, the manufacturing effect of this promising technology is discussed by examining the various implementations and nanomaterials now incorporated into 3DP technology at a commercial level [55].

1.10 Direct Ink Writing (DIW)

The most practical approach to 3DP hydrogels is likely to be direct ink writing (DIW) technology. Research into the joint creation of photosensitive hydrogel and DIW printing machinery for targeted application is ongoing. The advancement of a soluble photo catalyst that can be initiated under light waves is of significant importance for 3DP hydrogel innovation as it allows for the prevention of UV-induced skin damage. By choosing a surfactant with stacking ability, the water-based photoinitiator (Aqu-PI) can be formed via complex molecular arrangement with TPO without solvents emitting any volatile organic compounds (VOCs). Aqu-PI has a particle size of about 15 nm and an absorption peak in the visible region of the UV-visible spectrum. By combining Aqu-PI with monofunctional monomer and bifunctional monomer (BM), a photosensitive hydrogel can be made (MBA). The photosensitive hydrogel’s viscosity is increased after the addition of the tackifier, resulting in hydrogels with improved mechanical properties. The DIW printer’s extrusion device uses a 405 nm LED laser to photocure the photosensitive hydrogel, creating the interlayer connectivity structure and a 3D printout hydrogel that matches the digital production model’s structure [56].

1.11 Hull of Soy

The lack of material diversity and subpar robotic, heat, and rheology properties severely restrict the FFF technique’s potential uses. Most FFF filaments are made from either acrylonitrile butadiene styrene (ABS) or polylactic acid (PLA). Some fiberglass fiber components are used in the FFF process alongside pure thermoplastics. To obtain a diverse set of material properties to meet a variety of application needs, it is crucial to investigate materials for the FFF filaments. Polylactic acid serves as the matrix while soy hulls and soy protein isolate (SPI) are used as the fillers to create a bio-based and biodegradable fiberglass fiber material. The printability of the biocomposite filaments developed is evaluated using the FFF process, and a variety of soy hull-to-PLA blends are tested. As for the FFF filament, the amounts of soy hulls studied are 5 wt%, 7.5 wt%, and 10 wt%. In this study, we use experimental analysis and statistical methods to look into the effects of different soy hulls proportions on tensile, melt flow index (MFI), glass transition temperature (Tg), and storage modulus. Experiment results indicate that, of the three percentages tested, soy hulls at 5 wt% are the most effective. Furthermore, two fillers, SPI (at 2.5 wt%) and soy hulls (at 7.5 wt%), are mixed with PLA to examine the effects of each filler on these four properties. In this study, soy hulls and SPIbased bioproducts are successfully used to create FFF filaments. This research demonstrated the viability of 3DP with PLA, soy hulls, and SPI mixed biomaterials [57].

1.12 Laser Powder Bed Fusion

The NiTi-centered super elastic composite materials decorated with TiC nanoparticles were made using the laser powder bed fusion (LPBF) method. The high energy density of the laser beam prompted strong dispersion actions of carbon molecules from the TiC nano particles, resulting in the precipitation of TiCx and the associated Ni-rich even Ti-rich intermetallics, in comparison to the lack of reaction between TiC and NiTi observed in standard techniques. These nano precipitates were dispersed along the intricate connectivity of interconnected deformations, where they established an innovative cell membrane reinforcement framework on a submicron scale and helped to bring about a hierarchical diversity in the micro-structural. Submicron cellular fiber reinforced structure formation and TiC nano particle migration and distribution were then discussed in detail. According to the results, the rate at which the laser was scanned had a major impact on how the TiC nano particles were dispersed, the size of the cells, and the orientation of the matrix grains. Nano-TiC/NiTi composites fabricated using LPBF at the optimal parameter [58