Skip to main content
SpringerLink
Log in

Digital Twins for Smart Manufacturing

  • Chapter
  • First Online:
Digital Twins in Manufacturing

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

  • 1607 Accesses

Abstract

Smart manufacturing is a vital part of the broader concept of Industry 4.0. Its foundation is the bridge between virtual and physical environments, developed on the Internet of Things (IoT) and other contemporary technologies, such as cloud systems, data analytics, and machine learning. A cutting process controlled by digital twins can be a modern solution for manufacturing. To ensure the correct behavior of a complex manufacturing system, modern engineering uses model-based simulation and data analysis to predict the outcome, optimize, adjust, and evaluate at all stages, not only in the initial design, but also in the development, production, and monitoring phases. Such continuous data collection using virtual twin simulation and physical twin experimentation is related to modified vibratory turning and drilling tool structures, macro- and micro-drilling processes, improving the quality of grinding operations, and the application of Artificial Intelligence (AI) prediction methods for robotic sheet forming. As applications of the latter process gain momentum, solutions associated with local heating of the polymer sheet become more acceptable than expensive 3D printing processes, while the replacement of eco-unfriendly lubrication by ultrasonic metal sheet excitation allows the problems of the green economy to be addressed more quickly.

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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

Institutional subscriptions

References

  1. Ostasevicius V, Gaidys R, Gylienė V, Jūrėnas V, Daniulaitis V, Liepinaitis A (2011) Passive optimal tool structures for vibration cutting. J Vibroeng 13(4):769–777

    Google Scholar 

  2. Haug EJ, Arora JS (1979) Applied optimal design: mechanical and structural systems. Wiley, NY, p 506

    Google Scholar 

  3. Ostasevicius V et al, Twist drill with variable cross-section. SU invention No. 1357151

    Google Scholar 

  4. Ostasevicius V, Jurenas V, Juskevicius A (2014) Modified tool structures for effective cutting. Mechanika 20(2):171–176

    Article  Google Scholar 

  5. Bathe KJ, Wilson EL (1976) Numerical methods in finite element analysis. Prent-Hall, Englewood Cliffs, NJ USA, p 528

    Google Scholar 

  6. Ostasevicius V et al, Internal turning tool holder, SU invention Nr.1590206

    Google Scholar 

  7. Standards Committee of the IEEE Ultrasonics (1988) Ferroelectrics and frequency control society. In: IEEE standard on piezoelectricity. American National Standards Institute, NY

    Google Scholar 

  8. Ostasevicius V, Gaidys R, Rimkeviciene J, Dauksevicius R (2010) An approach based on tool mode control for surface roughness reduction in high-frequency vibration cutting. J Sound Vibr 329:4866–4879

    Article  Google Scholar 

  9. Rimkeviciene J, Gaidys R, Jurenas V, Ostasevicius V (2009) Research of ultrasonic assisted turning tool. J Vibroeng 11(1):34–40

    Google Scholar 

  10. Rimkeviciene J, Ostasevicius V, Jurenas V, Gaidys R (2009) Experiments and simulations of ultrasonically assisted turning tool. Mechanika 75(1):42–46

    Google Scholar 

  11. Jurenas V, Bubulis A, Skiedraite I, Ostasevicius V, Grazevicute J (2010) Tool jaw with ultrasonic transducer. Patent of Rep Lithuania B23C 9/00 No. LT 5586 B: 5

    Google Scholar 

  12. Ostasevicius V, Jurenas V, Grazevicute J, Bubulis A, Skiedraite I (2010) Quality control system of processed surface, Patent of Rep Lithuania B23B 1/00 No. LT 5534 B: 6

    Google Scholar 

  13. Vaicekauskis M, Gaidys R, Ostasevicius V (2013) Influence of boundary conditions on the vibration modes of the smart turning tool. Mechanika 3:296–300

    Google Scholar 

  14. Ostasevicius V, Ubartas M, Gaidys R, Jurenas V, Samper S, Dauksevicius R (2012) Numerical—Experimental identification of the most effective dynamic operation mode of a vibration drilling tool for improved cutting performance. J Sound and Vibr 331:5175–5190

    Article  Google Scholar 

  15. Ubartas M, Ostasevicius V, Jurenas V, Gaidys R (2010) Experimental nnvestigation of vibrational drilling. In: Mechanika 2010: proceedings of the 15th international conference, pp 445–449

    Google Scholar 

  16. Ubartas M, Ostasevicius V, Samper S, Jurenas V, Dauksevicius R (2011) Experimental investigation of vibrational drilling. Mechanika 17(4):368–373

    Article  Google Scholar 

  17. Ubartas M, Ostasevicius V, Jurenas V, Dauksevicius R (2012) Experimental investigation of vibrational tool dynamics. In: Mechanika 2012: proceedings of the 17th international conference, 322–326

    Google Scholar 

  18. Bayly P, Metzler S, Schaut A, Young K (2001) Theory of torsional chatter in twist drills: model, stability analysis and composition to test. J Man Sci and Eng 123(4):552–561

    Article  Google Scholar 

  19. Gylienė V, Ostasevicius V, Ubartas M (2013) Drilling process modelling using SPH. FEA Inf Eng J 2(6):1–6

    Google Scholar 

  20. Gylienė V, Ostasevicius V (2011) Cowper-Symonds material deformation law application in material cutting process using LS-Dyna FE code: turning and milling. In: 8th European LS-DYNA users conference, p 11

    Google Scholar 

  21. Ostasevicius V, Jurenas V, Vilkauskas A, Balevicius G, Senkus A, Jotautienė E (2017) A novel excitation approach to ultrasonically-assisted cylindrical grinding. Stroj Vest J Mech Eng 63(12):696–704

    Google Scholar 

  22. Ostasevicius V, Jurenas V, Balevicius G, Cesnavicius R (2020) Development of actuators for ultrasonically assisted grinding of hard brittle materials. Int J Adv Man Techn 106:289–301

    Article  Google Scholar 

  23. Ostasevicius V, Jurenas V, Eidukynas D, Grigaliunas V, Gudauskis M, Paleviciute I, Ambrasas V (2020) Pecularities of the robotized incremental metal and polimer sheets forming. In: 15th international conference on mechnical system and material MSM'2020 proceedings, IEEE Xplore 19996245, pp 1–3

    Google Scholar 

  24. Ostasevicius V, Paulauskaite-Taraseviciene A, Paleviciute I, Jurenas V, Griskevicius P, Eidukynas D, Kizauskiene L (2022) Investigation of the robotized incremental metal-sheet forming process with ultrasonic excitation. Materials 15(3):1024

    Google Scholar 

  25. Kumar VC, Hutchings IM (2004) Reduction of the sliding friction of metals by the application of longitudinal or transverse ultrasonic vibration. Tribol Int 37:833–840

    Article  Google Scholar 

  26. Ostasevicius V, Jurenas V, Grigaliūnas V, Eidukynas D, Bubulis A, I.Paleviciute I (2020) Incremental forming machine for sheet metal parts. Patent Appl LT2020 516:6

    Google Scholar 

  27. Bhattacharya A, Maneesh K, Venkata Reddy N, Cao J (2011) Formability and surface finish studies in single point incremental forming. J Manu Sci Eng 133(6):8

    Google Scholar 

  28. Stoughton TB, Yoon JW (2011) A new approach for failure criterion for sheet metals. Int J Plast 27(3):440–459

    Article  Google Scholar 

  29. Jawale K, Duarte JF, Reis A, Silva MB (2018) Microstructural investigation and lubrication study for single point incremental forming of copper. Int J Sol Str 151:145–151

    Article  Google Scholar 

  30. Gorji M, Berisha B, Manopulo N, Hora P (2016) Effect of through thickness strain distribution on shear fracture hazard and its mitigation by using multilayer aluminum sheets. J Mat Proc Techn 232:19–33

    Article  Google Scholar 

  31. Bouffioux C, Lequesne C, Vanhove H, Duflou JR, Pouteau P, Duchêne L, Habraken AM (2011) Experimental and numerical study of an AlMgSc sheet formed by an incremental process. J Mat Proc Techn 211(11):1684–1693

    Article  Google Scholar 

  32. Silva MB, Skjødt M, Atkins AG, Bay N, Martins PAF (2008) Single-point incremental forming and formability-failure diagrams. J Str Anal for Eng Des 43(1):15–35

    Article  Google Scholar 

  33. Allwood JM, Shouler DR, Tekkaya AE (2007) The increased forming limits of incremental sheet forming processes. Key Eng Mat 344:621–628

    Google Scholar 

  34. Emmens WC, Van den Boogaard AH (2007) Strain in shear, and material behaviour in incremental forming. Key Eng Mat 344:519–526

    Article  Google Scholar 

  35. Ostasevicius V, Paleviciute I, Paulauskaite-Taraseviciene A, Jurena V, Eidukynas D, Kizauskiene L (2022) Comparative analysis of machine learning methods for predicting robotized incremental metal sheet forming force. Sensors 22(18):22

    Google Scholar 

  36. Hauke J, Kossowski TM (2011) Comprison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaest Geogr 30(2):87–93

    Article  Google Scholar 

  37. Browne MW (2000) Cross-validation methods. J Math Psych 44(1):108–132

    Google Scholar 

  38. Pietersma A, Lacroixa R, Lefebvre D, Wade K (2003) Performance analysis for machine-learning experiments using small data sets. Comp Electr in Agric 38(1):1–17

    Article  Google Scholar 

  39. Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PlosOne 14(11):20

    Article  Google Scholar 

  40. Torgyn S, Lowe D, Daga S, Briggs D, Higgins R, Khovanova NA (2015) Machine learning for predictive modelling based on small data. Biomed Eng 48(20):469–474

    Google Scholar 

  41. Shaikhina T, Khovanova N, Mallick K (2014) Artificial neural networks in hard tissue engineering: another look at age-dependence of trabecular bone properties in osteoarthritis. IEEE EMBS Int Conf Biomed Health Inform, 484–487

    Google Scholar 

  42. Li Y, Shami A (2020) On hyperparameter optimization of machine learning algorithms. Theor Pract Neurocomp 415:295–31642

    Google Scholar 

  43. Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH (2019) Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Techn 17(1):26–40

    Google Scholar 

  44. Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peer J Comput Sci 7:e623

    Google Scholar 

  45. Najm SM, Paniti I (2021) Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF. Int J Adv Manuf Technol 114:2591–2615

    Article  Google Scholar 

  46. Ostasevicius V, Eidukynas D, Jurenas V, Paleviciute I, Gudauskis M, Grigaliunas V (2021) Investigation of advanced robotized polymer sheet incremental forming process. Sensors 21(3137):1–25

    Google Scholar 

  47. Rochling Industrial Materials. https://www.roechling.com/industrial/materials/thermoplastics/detail/trovidur-esa-d-261

  48. Ostasevicius V, Jurenas V, Eidukynas D, Grigaliunas V, Gudauskis M, Bubulis A, Paleviciute I, Ambrasas V (2020) Incremental forming machine for sheet plastic parts. Patent Appl LT2020 528:6

    Google Scholar 

  49. Ostasevicius V, Jurenas V, Bubulis A, Eidukynas D, Paulauskaite-Taraseviciene A, Paleviciute I (2021) Sheet parts incremental forming device. Patent Appl LT2021 549:6

    Google Scholar 

  50. Littmann W, Storck H, Walaschek J (2001) Reduction of friction using piezoelectric vibrations. Smart Str Mat: Damp Isol Int Soc Opt Phot 4331: 302–311

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mechatronics, Kaunas University of Technology, LT-51424, Kaunas, Lithuania

    Vytautas Ostaševičius

Authors
  1. Vytautas Ostaševičius
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vytautas Ostaševičius .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ostaševičius, V. (2022). Digital Twins for Smart Manufacturing. In: Digital Twins in Manufacturing. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-98275-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98275-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98274-4

  • Online ISBN: 978-3-030-98275-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation