Skip to main content
SpringerLink
Log in

Machine Learning as a Smart Manufacturing Tool

  • Conference paper
  • First Online:
Proceedings of International Conference on Intelligent Manufacturing and Automation

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

In smart manufacturing, machine learning is used in various manufacturing fields at various stages such as for future prediction in the manufacturing system, pattern recognition, fault detection, quality control and monitoring. Machine learning (ML) is used for classification and regression purpose which can be achieved using the past data. Machine learning algorithms and combination of algorithms are widely used in various machining processes. This paper reviews different machine learning algorithms used for specific applications in the product life cycle.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  1. Wu D, Jennings C, Terpenny J, Gao RX, Kumara S (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng 139

    Google Scholar 

  2. Ahuett-Garza H, Kurfess T (2018) A brief discussion on the trends of habilitating technologies for industry 4.0 and smart manufacturing. Manuf Lett 15:60–63

    Google Scholar 

  3. Shang C, You F (2019) Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 1010–1016

    Google Scholar 

  4. Kim D-H, Kim TJY, Wang X. Kim M, Quan Y-J, Oh JW, Ahn S-H (2018) Smart machining process using machine learning: a review and perspective on machining industry. Int J Precis Eng MFG Green Technol 5(4):555–568

    Google Scholar 

  5. Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res Open Access J 4:23–45

    Google Scholar 

  6. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156

    Google Scholar 

  7. Guo S, Yu J, Liu X, Wang C, Jiang Q (2019) A predicting model for properties of steel using the industrial big data based on machine learning. Comput Mater Sci 160:95–104

    Article  Google Scholar 

  8. Fischer CC, Tibbetts KJ, Morgan D, Ceder G (2006) Predicting crystal structure by merging data mining with quantum mechanics. Nat Mater 5:641–646

    Google Scholar 

  9. du Preez A, Oosthuizen GA (2019) Machine learning in cutting processes as enabler for smart sustainable manufacturing. Procedia Manuf 33:810–817

    Article  Google Scholar 

  10. Garcia-Ordas M (2017) Wear characterization of the cutting tool in milling processes using shape and texture descriptors. Ph.D. thesis, Universidad de Leon

    Google Scholar 

  11. Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tools Manuf 45(3):241–249

    Google Scholar 

  12. Wu D, Jennings C, Terpenny J, Gao RX, Kumara S (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng 139(7)

    Google Scholar 

  13. Jurkovic Z, Cukor G, Brezocnik M, Brajkovic T (2016) A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J Intell Manuf. https://doi.org/10.1007/s10845-016-1206-1

    Article  Google Scholar 

  14. D’Addona DM, Ullah AS, Matarazzo D (2017) Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J Intell Manuf 28(6):1285–1301

    Google Scholar 

  15. Karam S, Centobelli P, D’Addona DM, Teti R (2016) Online prediction of cutting tool life in turning via cognitive decision making. Procedia CIRP 41:927–932

    Article  Google Scholar 

  16. Zhang D, Bi G, Sun Z, Guo Y (2015) Online monitoring of precision optics grinding using acoustic emission based on support vector machine. Int J Adv Manuf Technol 80:761–774

    Article  Google Scholar 

  17. Shaban Y, Yacout S, Balazinski M, Meshreki M, Attia H (2015) Diagnosis of machining outcomes based on machine learning with logical analysis of data. In: Proceedings of the international conference on industrial engineering and operations management (IEOM), pp. 1–8

    Google Scholar 

  18. Saravanamurugan S, Thiyagu S, Sakthivel N, Nair BB (2017) Chatter prediction in boring process using machine learning technique. Int J Manuf Res 12:405–422

    Article  Google Scholar 

  19. Caydaş U, Hascalık A (2008) A study on surface roughness in abrasive water jet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582

    Article  Google Scholar 

  20. Majumder A (2015) Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters. Proc Inst Mech Eng Part B: J Eng Manuf 229:1504–1516

    Article  Google Scholar 

  21. Rao RV, Kalyankar V (2011) Parameters optimization of advanced machining processes using TLBO algorithm. In: International conference on, project, and production management (EPPM), pp 20–21. Science Direct, Singapore

    Google Scholar 

  22. Shi D, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Sig Proc 21(4):1799–1814

    Google Scholar 

  23. Ravikumar S, Ramachandran KI, Sugumaran V (2011) Machine learning approach for automated visual inspection of machine components. Expert Syst Appl 38:3260–3266

    Google Scholar 

  24. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Sig Proc 108:33–47

    Google Scholar 

  25. Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2013) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 1–19

    Google Scholar 

  26. Medjaher K, Tobon-Mejia DA, Zerhouni N (2012) Remaining useful life estimation of critical components with application to bearings. IEEE Trans Reliab 61(2):292–302

    Article  Google Scholar 

  27. Ribeiro B (2005) Support vector machines for quality monitoring in a plastic injection molding process. IEEE Trans Syst Man Cybern Part C: Appl Rev 35

    Google Scholar 

  28. Loyer JL, Henriques E, Fontul M, Wiseall S (2016) Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. Int J Prod Econ 178:109–119

    Google Scholar 

  29. Luo M, Xu Z, Chan HL, Alavi M (2013) Online predictive maintenance approach for semiconductor equipment. In: Proceedings of the 39th IEEE annual conference of the industrial electronics society (IECON) pp 3662–3667

    Google Scholar 

  30. Heng A, Tan AC, Mathew J, Montgomery N, Banjevic D, Jardine AK (2009) Intelligent condition-based prediction of machinery reliability. Mech Syst Sig Proc 23:1600–1614

    Google Scholar 

  31. Susto GA, Schirru A, Pampuri S (2015) Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans Ind Inform 2(3)

    Google Scholar 

  32. Tseng TL, Aleti KR, Hu Z, Kwon YJ (2016) E-quality control: a support vector machines approach. J Comput Des Eng 3(2):91–10

    Google Scholar 

  33. Koksal G, Batmaz I, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38:13448–13467

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Production Engineering Department, Fr. Conceicao Rodrigues College of Engineering, Bandra, Mumbai, India

    Bhushan T. Patil

  2. Mechanical Engineering Department, Fr. Conceicao Rodrigues College of Engineering, Bandra, Mumbai, India

    Meera B. Kokate & Geetha Subramanian

Authors
  1. Meera B. Kokate
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhushan T. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geetha Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meera B. Kokate .

Editor information

Editors and Affiliations

  1. Dwarkadas J. Sanghvi College of Engineering, Mumbai, India

    Hari Vasudevan

  2. Dwarkadas J. Sanghvi College of Engineering, Mumbai, India

    Vijaya Kumar N. Kottur

  3. RWTH Aachen University, Aachen, Germany

    Amool A. Raina

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kokate, M.B., Patil, B.T., Subramanian, G. (2020). Machine Learning as a Smart Manufacturing Tool. In: Vasudevan, H., Kottur, V., Raina, A. (eds) Proceedings of International Conference on Intelligent Manufacturing and Automation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4485-9_37

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4485-9_37

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4484-2

  • Online ISBN: 978-981-15-4485-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation