Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Design framework for model-based self-optimizing manufacturing systems

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Designing manufacturing systems requires a profound understanding of the manufacturing process and its challenges to meet final customer requirements. Considering future objectives already at an early design stage increases the flexibility of the manufacturing system and its robustness regarding changed boundary conditions. Today’s manufacturing systems rather control machine settings than process variables or even product quality. The major barrier for quality control is that in most manufacturing processes, quality cannot be measured on-line. Model-based self-sptimization (MBSO) has been developed to overcome this limitation. A combination of embedded process knowledge and tailored sensor integration enables for on-line quality estimation. The overall objective is to control key characteristics of product quality in a broad manufacturing landscape. This work describes a guideline of how to design an MBSO system with examples at each stage of the development process.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adams O (2016) Modellbasierte prädiktive Kraftregelung beim fräsen: Model based predictive force control in milling, Technologie der fertigungsverfahren, vol. Band 29/2016, 1. auflage edn. Apprimus Verlag, Aachen

    Google Scholar 

  2. Altintas Y, Lee P (1996) A general mechanics and dynamics model for helical end mills. CIRP Ann Manuf Technol 45(1):59–64. https://doi.org/10.1016/S0007-8506(07)63017-0

    Article  Google Scholar 

  3. Altintas Y, Ma CCH (1990) Direct adaptive control of milling force. Proceedings of the IEEE International Workshop on Intelligent Motion Control 1:13–20. https://doi.org/10.1109/IMC.1990.687288

    Article  Google Scholar 

  4. Auerbach T, Gierlings S, Veselovac D, Seidner R, Kamps S, Klocke F (2015) Concept for a technology assistance system to analyze and evaluate materials and tools for milling. In: ASME Turbo expo 2015: Turbine technical conference and exposition, p. V006T21A006, DOI https://doi.org/10.1115/GT2015-43209, (to appear in print)

  5. Bemporad A (2006) Model predictive control design: new trends and tools. In: Proceedings of the 45th IEEE conference on decision and control, pp 6678–6683, DOI https://doi.org/10.1109/CDC.2006.377490, (to appear in print)

  6. Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control charts: an overview. Qual Reliab Eng Int 23(5):517–543. https://doi.org/10.1002/qre.829

    Article  Google Scholar 

  7. Birkefeld K (2013) Virtuelle Optimierung von Geflecht-Preforms unter berücksichtigung von Fertigungsaspekten: Zugl.: Stuttgart, Univ., Diss., 2013, 1. aufl. edn. IFB Institut für Flugzeugbau. Universität Stuttgart. Verl. Dr Hut, München

  8. Brecher C, Özdemir D. (eds) (2017) Integrative production technology: theory and applications. Springer International Publishing, Cham

  9. Byrne G, Dornfeld D, Inasaki I, Ketteler G, König W., Teti R (1995) Tool condition monitoring (TCM)—the status of research and industrial application. CIRP Ann 44 (2):541–567. https://doi.org/10.1016/S0007-8506(07)60503-4

    Article  Google Scholar 

  10. Campocasso S, Poulachon G, Bissey-Breton S, Costes JP, Outeiro J.C. (2017) Towards cutting force evaluation without cutting tests. CIRP Ann 66(1):77–80. https://doi.org/10.1016/j.cirp.2017.04.023

    Article  Google Scholar 

  11. Chen JH, Sheu LJ, Chen WC, Chen HK, Chen CT (2008) Application of advanced process control in plastic injection molding. In: IEEE international conference on service operations and logistics, and informatics 2008, pp 2719–2724. https://doi.org/10.1109/SOLI.2008.4682997

  12. Chen Z, Turng LS (2007) Injection molding quality control by integrating weight feedback into a cascade closed-loop control system. Polym Eng Sci 47 (6):852–862. https://doi.org/10.1002/pen.20769

    Article  Google Scholar 

  13. Chryssolouris G (1992) Manufacturing systems: theory and practice. Springer Texts in Mechanical Engineering. Springer, New York. https://doi.org/10.1007/978-1-4757-2213-0

    Book  Google Scholar 

  14. Colledani M, Tolio T, Fischer A, Iung B, Lanza G, Schmitt R, Váncza J (2014) Design and management of manufacturing systems for production quality. CIRP Ann Manuf Technol 63(2):773–796. https://doi.org/10.1016/j.cirp.2014.05.002

    Article  Google Scholar 

  15. Daneshmend LK, Pak HA (1986) Model reference adaptive control of feed force in turning. J Dyn Syst Meas Control 108(3):215–222. https://doi.org/10.1115/1.3143770

    Article  MATH  Google Scholar 

  16. Dean A, Morris M, Stufken J, Bingham D (2015) Handbook of design and analysis of experiments. Chapman & Hall/CRC handbooks of modern statistical methods. CRC Press a Chapman & Hall book, Boca Raton and London and New York

    MATH  Google Scholar 

  17. Dewantoro G, Feriyonika F (2011) Model reference adaptive control of cavity pressure in injection molding during filling and packing phases. In: 2nd international conference on instrumentation control and automation (ICA), pp 47–52. https://doi.org/10.1109/ICA.2011.6130128

  18. Fei NC, Mehat NM, Kamaruddin S (2013) Practical applications of Taguchi method for optimization of processing parameters for plastic injection moulding: a retrospective review. ISRN Ind Eng 2013(1526):1–11. https://doi.org/10.1155/2013/462174

    Google Scholar 

  19. Friedman A, Jiang LS (1984) A Stefan-Signorini problem. J Differ Equ 51(2):213–231. https://doi.org/10.1016/0022-0396(84)90108-6

    Article  MathSciNet  MATH  Google Scholar 

  20. Fussell BK, Srinivasan K (1991) Adaptive control of force in end milling operations—an evaluation of available algorithms. J Manuf Syst 10(1):8–20. https://doi.org/10.1016/0278-6125(91)90043-2

    Article  Google Scholar 

  21. Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M (2015) Cloud-enabled prognosis for manufacturing. CIRP Ann 64(2):749–772. https://doi.org/10.1016/j.cirp.2015.05.011

    Article  Google Scholar 

  22. Gloy YS, Cloppenburg F, Gries T (2017) Integration of the vertical warp stop motion positioning in the model-based self-optimization of the weaving process. Int J Adv Manuf Technol 90 (9):3619–3632. https://doi.org/10.1007/s00170-016-9666-1

    Article  Google Scholar 

  23. Gloy YS, Sandjaja F, Gries T (2015) Model based self-optimisation of the weaving process. CIRP J Manuf Sci Technol CIRP-JMST 9:88–96. https://doi.org/10.1016/j.cirpj.2015.01.001

    Article  Google Scholar 

  24. Havlicsek H, Alleyne A (1999) Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning. IEEE/ASME Trans Mechatron 4(3):312–323. https://doi.org/10.1109/3516.789689

    Article  Google Scholar 

  25. Hering E (2010) Sensoren: Funktionsweise und Einsatzgebiete, 1. aufl. edn. Praxis. Vieweg, F, Wiesbaden

  26. Hermanns T, Schulz W, Vossen G, Thombansen U (2015) Mathematical modelling and linear stability analysis of laser fusion cutting. In: [International conference on numerical analysis and applied mathematics 2014, ICNAAM 2015, 22.09.2015-28.09.2015, rhodes, greece], AIP conference proceedings, vol. 1738, p. 370002. American Institute of Physics, Melville. https://doi.org/10.1063/1.4952147

  27. Hopmann C, Ressmann A, Reiter M, Stemmler S, Abel D (2016) A self-optimising injection moulding process with model-based control system parameterisation. Int J Comput Integr Manuf 29(11):1190–1199. https://doi.org/10.1080/0951192X.2015.1066035

    Article  Google Scholar 

  28. Keitzel G (2016) Präventive Parameteranpassung zur Sicherstellung des Spanabtransports beim Einlippentiefbohren, Technologie der fertigungsverfahren, vol. 2016, Band 27, 1. auflage edn. Apprimus Verlag Aachen

  29. Kienzle O (1952) Die Bestimmung von kräften und Leistungen an spanenden Werkzeugmaschinen VDI-z 96

  30. Klocke F, Abel D, Gries T, Hopmann C, Loosen P, Poprawe R, Reisgen U, Schmitt R, Schulz W, Abels P, Adams O, Auerbach T, Bobek T, Buchholz G, Döbbeler B, Frank D, Heinisch J, Hermanns T, Gloy YS, Keitzel G, Kemper M, Suarez Martel D, Reimer V, Reiter M, Saggiomo M, Schwenzer M, Stemmler S, Stoyanov S, Thombansen U, Veselovac D, Willms K (2017). In: Brecher C, Özdemir D (eds) Self-optimizing production technologies. Springer International Publishing, Cham, pp 745–875. https://doi.org/10.1007/978-3-319-47452-6_9

  31. Koren Y, Masory O, Malkin S (1981) Adaptive control with process estimation. CIRP Ann Manuf Technol 30(1):373–376. https://doi.org/10.1016/S0007-8506(07)60960-3

    Article  Google Scholar 

  32. Landers RG, Ulsoy AG, Ma YH (2004) A comparison of model-based machining force control approaches. Int J Mach Tools Manuf 44 (7–8):733–748. https://doi.org/10.1016/j.ijmachtools.2004.02.005

    Article  Google Scholar 

  33. Maybeck PS (Unknown Month 1979) Stochastic models, estimation, and control. Academic Press, New York

  34. Nolzen H, Isermann R (1995) Adaptive control of the cutting power for milling operation. American Control Conference, Proceedings of the 1995 3:2185–2189. https://doi.org/10.1109/ACC.1995.531287

    Article  Google Scholar 

  35. Permin E, Bertelsmeier F, Blum M, Bützler J., Haag S, Kuz S, Özdemir D., Stemmler S, Thombansen U, Schmitt R, Brecher C, Schlick C, Abel D, Poprawe R, Loosen P, Schulz W, Schuh G (2016) Self-optimizing production systems. Procedia CIRP 41(Supplement C):417–422. https://doi.org/10.1016/j.procir.2015.12.114

    Article  Google Scholar 

  36. Powell M (2009) The BOBYQA algorithm for bound constrained optimization without derivatives. http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf. DAMTP 2009/NA06

  37. Rao RV (2011) Advanced modeling and optimization of manufacturing processes: international research and development. Springer series in advanced manufacturing. Springer, London

    Book  Google Scholar 

  38. Reimer V, Alptekin S, Gries T (2016) Investigation of the relations between the parameters in the radial braiding process. In: Kyosev Y. (ed) Recent developments in braiding and narrow weaving. Springer International Publishing, Cham, pp 111–120. https://doi.org/10.1007/978-3-319-29932-7_11

  39. Reimer V, Persiyanov-Dubrov M, Dawson J, Gries T (2016) Developing control systems for the radial over-braiding process. In: Proceeding of the 17th European conference on composite materials - ECCM17, vol 17

  40. Reisgen U, Willms K, Herfert D (2015) Optimierung von Prozessparametern beim automatisierten MSG-Schweißen durch die inverse Nutzung von Ersatzmodellen. Schweissen und Schneiden 67(10):602–609. http://publications.rwth-aachen.de/record/540785

    Google Scholar 

  41. Richalet J (1993) Industrial application of model based predictive control. Automatica 29:1251–1274

    Article  MathSciNet  Google Scholar 

  42. Rockel H (2006) Analyse und Synthese parametrischer Iterativ Lernender Regelungen: Zugl.: Clausthal, Techn. Univ., Diss., 2006, Fortschritt-Berichte VDI Reihe 8, Mess-, Steuerungs- und regelungstechnik, vol. 1103, als ms. gedr edn. VDI-Verl., Düsseldorf

  43. Saggiomo M, Kemper M, Gloy YS, Gries T (2016) Weaving machine as cyber-physical production system. In: Proceedings of the 2016 IEEE international conference on industrial technology (ICIT)

  44. Saravanan R, Asokan P, Sachithanandam M (2001) Comparative analysis of conventional and non-conventional optimisation techniques for CNC turning process. Int J Adv Manuf Technol 17(7):471–476. https://doi.org/10.1007/s001700170146

    Article  Google Scholar 

  45. Schwenzer M, Adams O, Klocke F, Stemmler S, Abel D (2017) Model-based predictive force control in milling: determination of reference trajectory. Prod Eng 11(2):107–115. https://doi.org/10.1007/s11740-017-0721-z. http://link.springer.com/10.1007/s11740-017-0721-z

    Article  Google Scholar 

  46. Schwenzer M, Auerbach T, Döbbeler B., Klocke F (2018) Force model identification in milling—an algorithmic review, San Sebastian

  47. Shewhart WA (2012) Statistical method from the viewpoint of quality control. Dover Publications. http://public.eblib.com/choice/publicfullrecord.aspx?p=1919684. OCLC: 869525873

  48. Stemmler S, Abel D, Adams O, Klocke F (2016) Model predictive feed rate control for a milling machine. 8th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2016Troyes, France, 28—30 June 2016 49(12):11–16. https://doi.org/10.1016/j.ifacol.2016.07.542. http://www.sciencedirect.com/science/article/pii/S2405896316307959

    Google Scholar 

  49. Stemmler S, Abel D, Schwenzer M, Adams O, Klocke F (2017) Model predictive control for force control in milling. IFAC-PapersOnLine 50(1):15,871–15,876. https://doi.org/10.1016/j.ifacol.2017.08.2336. http://linkinghub.elsevier.com/retrieve/pii/S2405896317331518

    Article  Google Scholar 

  50. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739. https://doi.org/10.1016/j.cirp.2010.05.010

    Article  Google Scholar 

  51. Thombansen U, Hermanns T, Molitor T, Pereira M, Schulz W (2014) Measurement of cut front properties in laser cutting. Phys Procedia 56(Supplement C):885–891. https://doi.org/10.1016/j.phpro.2014.08.107

    Article  Google Scholar 

  52. Thombansen U, Purrio M, Buchholz G, Hermanns T, Molitor T, Willms K, Schulz W, Reisgen U (2016) Determination of process variables in melt-based manufacturing processes. Int J Comput Integr Manuf 29 (11):1147–1158. https://doi.org/10.1080/0951192X.2016.1145799

    Article  Google Scholar 

  53. Varela AE (2000) Self-tuning pressure control in an injection moulding cavity during filling. Chem Eng Res Des 78(1):79–86. https://doi.org/10.1205/026387600526906

    Article  Google Scholar 

  54. Woll SLB, Cooper DJ (1997) Pattern-based closed-loop quality control for the injection molding process. Polym Eng Sci 37(5):801–812. https://doi.org/10.1002/pen.11723

    Article  Google Scholar 

  55. Yang Y, Gao F (1999) Cycle-to-cycle and within-cycle adaptive control of nozzle pressure during packing-holding for thermoplastic injection molding. Polym Eng Sci 39(10):2042–2063. https://doi.org/10.1002/pen.11597

    Article  Google Scholar 

  56. Yanushevsky R, Minis I (1990) Optimal control of the cutting force in metal cutting operations. In: 1990 American control conference, pp 2947–2952

  57. Zheng HY, Brookfield DJ, Steen WM (1990) Experimental study of the relationship between in-process signals and cut quality in gas-assisted laser cutting. In: Opower H (ed) The hague ’90, 12-16 april, SPIE Proceedings, SPIE, pp 218–230. https://doi.org/10.1117/12.20547

Download references

Acknowledgements

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”.

Author information

Authors and Affiliations

  1. Intitute for Laser Technology (ILT), Fraunhofer, Steinbachstr. 15, 52074, Aachen, Germany

    Ulrich Thombansen & Reinhardt Poprawe

  2. Welding and Joining Institute (ISF), RWTH Aachen University, Pontstraße 49, 52062, Aachen, Germany

    Guido Buchholz & Uwe Reisgen

  3. Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Campus-Boulevard 30, 52074, Aachen, Germany

    Daniel Frank, Thomas Pullen, Max Schwenzer, Fritz Klocke & Robert Schmitt

  4. Institute of Plastics Processing (IKV) in Industry and the Skilled Crafts, RWTH Aachen University, Seffenter Weg 201, 52074, Aachen, Germany

    Julian Heinisch & Christian Hopmann

  5. Istitut für Textiltechnik (ITA), RWTH Aachen University, Otto-Blumenthal-Str. 1, 52074, Aachen, Germany

    Maximilian Kemper, Viktor Reimer & Thomas Gries

  6. Intitute for Production Technology (IPT), Fraunhofer, Steinbachstr. 17, 52074, Aachen, Germany

    Grigory Rotshteyn

  7. Istitute of Automatic Control (IRT), RWTH Aachen University, Campus-Boulevard 30, 52074, Aachen, Germany

    Sebastian Stemmler & Dirk Abel

Authors
  1. Ulrich Thombansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guido Buchholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Frank
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julian Heinisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maximilian Kemper
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas Pullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Viktor Reimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Grigory Rotshteyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Max Schwenzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sebastian Stemmler
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Dirk Abel
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Thomas Gries
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Christian Hopmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Fritz Klocke
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Reinhardt Poprawe
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Uwe Reisgen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Robert Schmitt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Max Schwenzer.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thombansen, U., Buchholz, G., Frank, D. et al. Design framework for model-based self-optimizing manufacturing systems. Int J Adv Manuf Technol 97, 519–528 (2018). https://doi.org/10.1007/s00170-018-1951-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-1951-8

Keywords

Search

Navigation