Skip to main content
SpringerLink
Log in

Modeling and Optimization of Mechanical Systems and Processes

  • Chapter
  • First Online:
Book cover Modern Mechanical Engineering

Part of the book series: Materials Forming, Machining and Tribology ((MFMT))

Abstract

This chapter reviews the most commonly used techniques used for modeling and optimizing mechanical systems and processes. Statistical and artificial intelligence based tools for modeling are summarized, pointing their advantages and shortcomings. Also, analytic, numeric and stochastic optimization techniques are briefly explained. Finally, two cases of study are developed in order to illustrate the use of these tools, the first one dealing with the modeling of the surface roughness in a drilling process and the other one, on the multi-objective optimization of a hot forging process.

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
Hardcover Book
USD 169.99
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. Aalst WMP, Rubin V, Verbeek HMW, Dongen BF, Kindler E, Günther CW (2010) Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model 9:87–111. doi:10.1007/s10270-008-0106-z

    Article  Google Scholar 

  2. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York

    MATH  Google Scholar 

  3. Foster J, Barkus E, Yavorky C (2006) Understanding and using advanced statistics. SAGE Publishers, London

    Google Scholar 

  4. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163. doi:10.1023/A:1007465528199

    Article  MATH  Google Scholar 

  5. Grafarend EW (2006) Linear and nonlinear models: fixed effects, random effects and mixed models. Walter de Gruyter, Berlin

    Google Scholar 

  6. Lewis SG, Raval A, Angus JE (2008) Bayesian Monte Carlo estimation for profile hidden Markov models. Math Comput Model 47:1198–1216. doi:10.1016/j.mcm.2007.07.002

    Article  MATH  MathSciNet  Google Scholar 

  7. Lyngsø RB, Pedersen CNS (2002) The consensus string problem and the complexity of comparing hidden Markov models. J Comput Syst Sci 65:545–569. doi:10.1016/S0022-0000(02)00009-0

    Article  Google Scholar 

  8. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim 26:369–395. doi:10.1007/s00158-003-0368-6

    Article  MATH  MathSciNet  Google Scholar 

  9. Miao Q, Huang HZ, Fan X (2007) A comparison study of support vector machines and hidden Markov models in machinery condition monitoring. J Mech Sci Technol 21:607–615. doi:10.1007/BF03026965

    Article  Google Scholar 

  10. Pernkopf N, Wohlmayr M (2013) Stochastic margin-based structure learning of Bayesian network classifiers. Pattern Recogn 46:4640471. doi:10.1016/j.patcog.2012.08.007

    Article  Google Scholar 

  11. Quiza R, Reis P, Davim JP (2006) Multi-objective optimization of cutting parameters for drilling laminate composite materials by using genetic algorithms. Compos Sci Technol 66:3083–3088. doi:10.1016/j.compscitech.2006.05.003

    Article  Google Scholar 

  12. Rivals I, Personnaz L (2000) Construction of confidence intervals for neural networks based on least squares estimation. Neural Netw 13:463–484. doi:10.1016/S0893-6080(99)00080-5

    Article  Google Scholar 

  13. Sarker R, Mohammadian M, Yao X (2003) Evolutionary optimization. Kluwer Academic Publishers, New York

    Google Scholar 

  14. Schneider JJ, Kirkpatrick S (2006) Stochastic optimization. Springer-Verlag, Berlin

    MATH  Google Scholar 

  15. Sha W (2006) Comment on prediction of the flow stress of 0.4C–1.9Cr–1.5Mn–1.0Ni–0.2Mo steel during hot deformation by R.H. Wu et al. [J. Mater. Process. Technol. 116 (2001) 211]. J Mater Process Tech 171:283–284. doi:10.1016/j.jmatprotec.2005.07.004

    Article  Google Scholar 

  16. Utyuzhnikov SV, Fantini P, Guenov MD (2009) A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization. J Comput Appl Math 223:820–841. doi:10.1016/j.cam.2008.03.011

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgment

The authors acknowledge the kind contribution of MSc. Rui Sendão and MSc. A. Festas in the execution of the experimental work on surface roughness of the titanium alloy drilling process.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Matanzas, Autopista a Varadero km 3½, 44740, Matanzas, Cuba

    Ramón Quiza & Gerardo Beruvides

  2. Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193, Aveiro, Portugal

    J. Paulo Davim

Authors
  1. Ramón Quiza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerardo Beruvides
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Paulo Davim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramón Quiza .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Quiza, R., Beruvides, G., Davim, J.P. (2014). Modeling and Optimization of Mechanical Systems and Processes. In: Davim, J. (eds) Modern Mechanical Engineering. Materials Forming, Machining and Tribology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45176-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-45176-8_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45175-1

  • Online ISBN: 978-3-642-45176-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation