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4 Adoptability of additive manufacturing process: design perceptive

  • S. Suresh , Parveen Kumar , T. Yuvaraj , D. Velmurugan and Elango Natarajan
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3D Printing Technologies
This chapter is in the book 3D Printing Technologies

Abstract

The application of additive manufacturing (AM) has expanded to previously unseen areas in business and academia. The ability to fabricate complex structures using AM includes everything from a part’s overall geometry to the topology of its internal structural design. Designing for AM presents a challenge along with its freedom of fabrication. The challenges include modeling and optimizing intricate geometries, integrating AM design knowledge into the product, and increasing the usability of design for AM tools. To successfully shape the future AM environment, reflecting on the most recent advancements in structural optimization and design for AM (DfAM) is essential. The recent development of three-dimensional (3D) printing allows researchers to produce complex structures, permitting unique production processes show standard synthetic engineering approaches in terms of resolution and control precision. Machine learning (ML) is a growing technology that focuses on enabling software to improve the process. ML approaches enhance the effectiveness of AM design, process, and the final product. This article provides a detailed review of the insights into DfAM concepts in AM. In the end, the most up-to-date applications of ML concepts in integrating DfAM are reviewed.

Abstract

The application of additive manufacturing (AM) has expanded to previously unseen areas in business and academia. The ability to fabricate complex structures using AM includes everything from a part’s overall geometry to the topology of its internal structural design. Designing for AM presents a challenge along with its freedom of fabrication. The challenges include modeling and optimizing intricate geometries, integrating AM design knowledge into the product, and increasing the usability of design for AM tools. To successfully shape the future AM environment, reflecting on the most recent advancements in structural optimization and design for AM (DfAM) is essential. The recent development of three-dimensional (3D) printing allows researchers to produce complex structures, permitting unique production processes show standard synthetic engineering approaches in terms of resolution and control precision. Machine learning (ML) is a growing technology that focuses on enabling software to improve the process. ML approaches enhance the effectiveness of AM design, process, and the final product. This article provides a detailed review of the insights into DfAM concepts in AM. In the end, the most up-to-date applications of ML concepts in integrating DfAM are reviewed.

Chapters in this book

  1. Frontmatter I
  2. Acknowledgments V
  3. Preface VII
  4. Contents XI
  5. List of contributors XV
  6. 1 3D-printed antennas 1
  7. 2 The recent developments in 3D bioprinting: a general bibliometric study and thematic investigation 39
  8. 3 Additive manufacturing of compositionally complex alloys: trends, challenges, and future perspectives 61
  9. 4 Adoptability of additive manufacturing process: design perceptive 77
  10. 5 Advanced bioprinting processes using additive manufacturing technologies: revolutionizing tissue engineering 95
  11. 6 Comparative analysis of thermal characteristics and optimizing laminar flow within medical-grade 3D printers for fabrication of sterile patientspecific implants (PSIs) using computational fluid dynamics 119
  12. 7 Review of 4D printing and materials enabling Industry 4.0 for implementation in manufacturing: an Indian context 143
  13. 8 Processing of smart materials by additive manufacturing and 4D printing 181
  14. 9 A comprehensive review on effect of DMLS process parameters and post-processing on quality of product in biomedical field 197
  15. 10 Finite element method investigation on delamination of 3D printed hybrid composites during the drilling operation 223
  16. 11 Analyzing the dimensional stability in direct ink written composite ink: a machine learning approach 235
  17. 12 Recent applications of rapid prototyping with 3D printing: a review 245
  18. 13 3D printing insight: techniques, application, and transformation 259
  19. 14 Additive manufacturing and 4D printing applications for Industry 4.0-enabled digital biomedical and pharmaceutical sectors 289
  20. 15 Application of three-dimensional printing in medical, agriculture, engineering, and other sectors 311
  21. 16 Recent developments in 3D printing: a critical analysis and deep dive into innovative real-world applications 335
  22. 17 Exploring design strategies for enhanced 3D printing performance 353
  23. Biographies 371
  24. Index 375
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