Skip to main content

Introduction to Advanced Manufacturing Processes and Optimization Methodologies

  • 207 Accesses

Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

Manufacturing can be defined as the application of mechanical, physical, and chemical processes to convert the geometry, properties, and/or shape of raw material into finished parts or products. This includes all intermediate processes required for the production and integration of the final product. Manufacturing involves interrelated activities which include product design, material selection, production process planning, production, quality assurance, management and marketing of products.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Armağan M, Arici AA (2017) Cutting performance of glass-vinyl ester composite by abrasivewater jet. Mater Manuf Processes 32(15):1715–1722

    Google Scholar 

  2. Asad ABMA, Masaki T, Rahman M, Lim HS, Wong YS (2007) Tool-based micro-machining. J Mater Process Technol 192:204–211

    Google Scholar 

  3. Bao W, Chen P, Tansel I, Reen NS, Yang S, Rincon D (2003) Selection of optimal cutting conditions by using the genetically optimized neural network system (GONNS). In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Artificial neural networks and neural information processing—ICANN/ICONIP 2003. ICANN 2003, ICONIP 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg

    Google Scholar 

  4. Bhattacharyya B, Gangopadhyay S, Sarkar BR (2007) Modelling and analysis of EDMed job surface integrity. J Mater Process Technol 189(1–3):169–177

    Google Scholar 

  5. Camposeco-Negrete C (2019) Prediction and optimization of machining time and surface roughness of AISI O1 tool steel in wire-cut EDM using robust design and desirability approach. Int J Adv Manuf Technol 1–12

    Google Scholar 

  6. Dang XP (2018) Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm. Mater Manuf Processes 33(4):397–404

    Google Scholar 

  7. Das MK, Kumar K, Barman TK, Sahoo P (2014) Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel. Procedia Mater Sci 6:741–751

    Google Scholar 

  8. Dewangan S, Gangopadhyay S, Biswas CK (2015) Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Eng Sci Technol Int J 18(3):361–368

    Google Scholar 

  9. Di Orio G, Cândido G, Barata J, Scholze S, Kotte O, Stokic D (2013) Self-learning production systems (SLPS)-optimization of manufacturing process parameters for the shoe industry. In: 2013 11th IEEE international conference on industrial informatics (INDIN). IEEE, pp 386–391

    Google Scholar 

  10. Dow TA, Scattergood RO (2003) Mesoscale and microscale manufacturing processes: challenges for materials, fabrication and metrology. In: Proceedings of the ASPE winter topical meeting, vol. 28. pp 14–19)

    Google Scholar 

  11. Durairaj M, Gowri S (2013) Parametric optimization for improved tool life and surface finish in micro turning using genetic algorithm. Procedia Eng 64:878–887

    Google Scholar 

  12. Filiz S, Ozdoganlar OB (2010) A model for bending, torsional, and axial vibrations of microand macro-drills including actual drill geometry-part 1: model development and numerical solution. J Manuf Sci Eng 132:041017–1–8

    Google Scholar 

  13. Ganapathy S, Balasubramanian P, Senthilvelan T, Kumar R (2019) Multi-response optimization of machining parameters in EDM using square-shaped nonferrous electrode. In: Adv Manuf Process. Springer, Singapore, pp 287–295

    Google Scholar 

  14. Gopalakannan S, Senthilvelan T (2014) Optimization of machining parameters for EDM operations based on central composite design and desirability approach. J Mech Sci Technol 28(3):1045–1053

    Google Scholar 

  15. Gopalsamy BM, Mondal B, Ghosh S (2009) Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel

    Google Scholar 

  16. Gostimirovic M, Pucovsky V, Sekulic M, Rodic D, Pejic V (2019) Evolutionary optimization of jet lag in the abrasive water jet machining. Int J Adv Manuf Technol 101(9–12):3131–3141

    Google Scholar 

  17. Gupta K, Jain NK, Laubscher R (2017) Chapter 4-advances in gear manufacturing. In: Advanced gear manufacturing and finishing, 67–125

    Google Scholar 

  18. Huo D, Cheng K, Wardle F (2010) Design of a five-axis ultra-precision micro-milling machine—ULTRAMIll. Part 1: holistic design approach, design considerations and specifications. Int J Adv Manuf Technol 47(9–12): 867–877

    Google Scholar 

  19. Jagadeesh T (2015) Non traditional machining. Mechanical Engineering Department, National Institute of Technology, Calicut

    Google Scholar 

  20. Jain VK (2008) Advanced (non-traditional) machining processes. In: Machining. Springer, London, pp 299–327

    Google Scholar 

  21. Kolli M, Kumar A (2015) Effect of dielectric fluid with surfactant and graphite powder on electrical discharge machining of titanium alloy using Taguchi method. Eng Sci Technol Int J 18(4):524–535

    Google Scholar 

  22. Kumar SL, Jerald J, Kumanan S, Aniket N (2014) Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput Appl 25(7–8):1941–1950

    Google Scholar 

  23. Malekian M, Park SS, Jun MBG (2009) Modeling of dynamic micro-milling cutting forces. Int J Mach Tools Manuf 49:586–598

    Google Scholar 

  24. Momber AW, Kovacevic R (2012) Principles of abrasive water jet machining. Springer Science& Business Media

    Google Scholar 

  25. Muthuramalingam T, Mohan B (2015) A review on influence of electrical process parameters in EDM process. Arch Civil Mech Eng 15(1):87–94

    Google Scholar 

  26. Palani S, Natarajan U, Chellamalai M (2013) On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS). Mach Vis Appl 24(1):19–32

    Google Scholar 

  27. Rahman M, Kumar AS, Prakash JRS (2001) Micro milling of pure copper. J Mater Process Technol 116(1):39–43

    Google Scholar 

  28. Rahman AA, Mamat A,Wagiman A (2009) Effect of machining parameters on hole quality of micro drilling for brass. Modern Appl Sci 3(5):221–230

    Google Scholar 

  29. Rao RV (2010) Advanced modelling and optimization of manufacturing processes: international research and development. Springer Science & Business Media

    Google Scholar 

  30. Schwartzentruber J, Narayanan C, Papini M, Liu HT (2016) Optimized abrasive waterjet nozzle design using genetic algorithms. In: The 23rd international conference on water jetting, At Seattle, USA

    Google Scholar 

  31. Shanmugam DK, Nguyen T, Wang J (2008) A study of delamination on graphite/epoxy composites in abrasive waterjet machining. Compos A Appl Sci Manuf 39(6):923–929

    Google Scholar 

  32. Shastri AS, Nargundkar A, Kulkarni AJ (2020) Multi-cohort intelligence algorithm for solving advanced manufacturing process problems. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04858-y

    CrossRef  Google Scholar 

  33. Shukla R, Singh D (2017) Experimentation investigation of abrasive water jet machining parameters using Taguchi and Evolutionary optimization techniques. SwarmEvolut Comput 32:167–183

    Google Scholar 

  34. Sofuoğlu MA, Çakır FH, Kuşhan MC, Orak S (2019) Optimization of different non-traditional turning processes using soft computing methods. Soft Comput 23(13):5213–5231

    Google Scholar 

  35. Teimouri R, Baseri H (2014) Optimization of magnetic field assisted EDM using the continuous ACO algorithm. Appl Soft Comput 14:381–389

    Google Scholar 

  36. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

    Google Scholar 

  37. Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37(6):4650–4659

    Google Scholar 

  38. Zain AM, Haron H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11(8):5350–5359

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Apoorva Shastri .

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shastri, A., Nargundkar, A., Kulkarni, A.J. (2021). Introduction to Advanced Manufacturing Processes and Optimization Methodologies. In: Socio-Inspired Optimization Methods for Advanced Manufacturing Processes. Springer Series in Advanced Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-15-7797-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-7797-0_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7796-3

  • Online ISBN: 978-981-15-7797-0

  • eBook Packages: EngineeringEngineering (R0)