Skip to main content
SpringerLink
Log in

Artificial Intelligence Tools

  • Chapter
  • First Online:
Hybrid Modeling and Optimization of Manufacturing

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSCOMPUTAT))

Abstract

This chapter summarizes the main concepts on artificial intelligence, remarking those tools which are commonly applied to the modeling and optimization of manufacturing processes. Special emphasis has been done on soft computing techniques, because of the wide use that these ones have in this field. Each of the main soft computing techniques (artificial neural networks, fuzzy logic and stochastic optimization) is explained and, examples of applications are given.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    This problem, which is not exclusive of the RBFN, is explained more detailedly in Sect. 3.2.7.

References

  • B.A. Attea, A fuzzy multi-objective particle swarm optimization for effective data clustering. Memetic Comp. 2, 305–312 (2010). doi:10.1007/s12293-010-0047-2

    Article  Google Scholar 

  • L. Behera, S. Kumar, A. Patnaik, On adaptive learning rate that guarantees convergence in feedforward networks. IEEE T Neural Netw. 17, 1116–1125 (2006). doi:10.1109/TNN.2006.878121

    Article  Google Scholar 

  • T. Bertsimas, D. Tsitsiklis, Simulated annealing. Stat. Sci. 8, 10–15 (1993). doi:10.1214/ss/1177011077

    Article  Google Scholar 

  • E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999)

    MATH  Google Scholar 

  • M.D. Buhmann, Radial Basis Functions (Cambridge University Press, Cambridge, 2004)

    Google Scholar 

  • G.A. Carpenter, S. Goldberg, Adaptive Resonance Theory, in Encyclopedia of Machine Learning, ed. by C. Sammut, G.I. Webb (Springer, New York, 2009)

    Google Scholar 

  • M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit, Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int. J. Adv. Manuf. Tech. 46, 445–464 (2010). doi:10.1007/s00170-009-2104-x

    Article  Google Scholar 

  • C.A. Coello, Multiobjective Optimization: Current and Future Challenges, in Advances in Soft Computing: Engineering, Design and Manufacturing, ed. by J. Benitez, O. Cordon, F. Hoffmann, R. Roy (Springer, London, 2003)

    Google Scholar 

  • M. Dorigo, C. Blum, Ant colony optimization theory: A survey. Lect. Notes Comput. Sci. 344, 243–278 (2005). doi:10.1016/j.tcs.2005.05.020

    MathSciNet  MATH  Google Scholar 

  • Flexer A Statistical evaluation of neural network experiments: minimum requeriments and current practices. In: Trappl R (ed.) 13th European Meeting on Cybernetics and Systems Research, Vienna (1996)

    Google Scholar 

  • R. Fullér, Introduction to neuro-fuzzy systems (Physica-Verlag, Heidelberg, 2000)

    MATH  Google Scholar 

  • H. Ghaiebi, M. Solimanpur, An ant algorithm for optimization of hole-making operations. Comput. Ind. Eng. 52, 308–319 (2007). doi:10.1016/j.cie.2007.01.001

    Article  Google Scholar 

  • M.H. Ghaseminezhad, A. Karami, A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Appl. Soft. Comput. 11, 3771–3778 (2011). doi:10.1016/j.asoc.2011.02.009

    Article  Google Scholar 

  • M.T. Hagan, H.B. Demuth, M. Beale, Neural Network Design (China Machine Press, Beijing, 2002)

    Google Scholar 

  • R.L. Harvey, Neural Network Principles (Prentice Hall, Englewood Cliffs, 1994)

    MATH  Google Scholar 

  • L.G. Heins, D.R. Tauirtz, Adaptive Resonance Theory(ART):An Introduction Internal Report 95–35 (Department of Computer Science, Leiden University, Leiden, 1995)

    Google Scholar 

  • Y.H. Hu, J.-N. Hwang, Handbook of Neural Network Signal Processing (CRC Press, Boca Raton, 2002)

    Google Scholar 

  • J.S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system. IEEE T Syst. Man Cyb. 23, 665–685 (1993). doi:10.1109/21.256541

    Article  Google Scholar 

  • N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering (MIT Press, Cambridge, 1998)

    Google Scholar 

  • A. López, C.A. Coello, Multi-objective evolutionary algorithms: a review of the state-of-the-art and some of their applications in chemical engineering, in Multi-Objective Optimization Techniques and Applications in Chemical Engineering, ed. by R.G. Pandu (World Scientific, Singapore, 2009)

    Google Scholar 

  • F. Marini, J. Zupan, A.L. Magri, Class-modeling using Kohonen artificial neural networks. Anal. Chim. Acta 544, 306–314 (2005). doi:10.1016/j.aca.2004.12.026

    Article  Google Scholar 

  • A. Noorul Haq, K. Sivakumar, R. Saravanan, K. Karthikeyan, Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly. Int. J. Adv. Manuf. Tech. 27, 865–869 (2006). doi:10.1007/s00170-004-2274-5

    Article  Google Scholar 

  • S.P. Po, The application of an ANFIS and grey system method in turning tool-failure detection. Int. J. Adv. Manuf. Tech. 19, 564–572 (2002). doi:10.1007/s001700200061

    Article  Google Scholar 

  • S. Rajasekaran, G.A. Vijayalakshmi Pal, Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications (Prentice-Hall of India, New Delhi, 2003)

    Google Scholar 

  • J. Ramík, Soft computing: Overview and Recent Developments in Fuzzy Optimization. University of Ostrava (Czech Republic) (2001), http://ac030.osu.cz/irafm/ps/softco01.pdf

  • T.J. Ross, Fuzzy Logic With Engineering Applications (Wiley, West Sussex, 2004)

    MATH  Google Scholar 

  • R. Rojas, Neural Networks: A Systematic Introduction (Springer, Berlin, 1996)

    Google Scholar 

  • G. Roussos, B.J.C. Baxter, Rapid evaluation of radia basis functions. J. Comput. Appl. Math. 180, 51–70 (2005). doi:10.1016/j.cam.2004.10.002

    Article  MathSciNet  MATH  Google Scholar 

  • R. Saravanan, R.S. Sankar, P. Asokan, K. Vijayakumar, G. Prabhaharan, Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques. Int. J. Adv. Manuf. Tech. 26, 30–40 (2005). doi:10.1007/s00170-003-1938-x

    Article  Google Scholar 

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

    Google Scholar 

  • W. Sha, K.L. Edwards, The use of artificial neural networks in materials science based research. Mater Design 28, 1747–1752 (2007). doi:10.1016/j.matdes.2007.02.009

    Article  Google Scholar 

  • W. Siler, J.J. Buckley, Fuzzy Expert Systems and Fuzzy Reasoning (Wiley, Hoboken, 2005)

    Google Scholar 

  • J.J. Schneider, S. Kirkpatrick, Stochastic Optimization (Springer, Berlin, 2006)

    MATH  Google Scholar 

  • M. Tajine, D. Elizondo, New methods for testing linear separability. Neurocomputing 47, 161–188 (2002). doi:10.1016/S0925-2312(01)00587-2

    Article  MATH  Google Scholar 

  • D.A. Van Veldhuizen, G.B. Lamont, Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8, 125–147 (2000). doi:10.1162/106365600568158

    Article  Google Scholar 

  • N. Zhang, An online gradient method with momentumnext term for two-layer feedforward neural networks. Appl. Math. Comput. 212, 488–498 (2009). doi:10.1016/j.camwa.2011.09.028

    Article  MathSciNet  MATH  Google Scholar 

  • Y. Zhang, T. Chai, H. Wang, A nonlinear control method based on ANFIS and multiple models for a class of SISO nonlinear systems and its application. IEEE T Neural Netw. 22, 1783–1795 (2011). doi:10.1109/TNN.2011.2166561

    Article  Google Scholar 

  • E. Zitzler, M. Laumanns, E. Bleuler, A tutorial on evolutionary multiobjective optimization, in Metaheuristics for Multiobjective Optimization, ed. by X. Gandibleux, M. Sevaux, K. Sörensen, V. T’kindt (Springer, Berlin, 2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Ramón Quiza

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

    Omar López-Armas

  3. 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. Omar López-Armas
    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

Rights and permissions

Reprints and permissions

Copyright information

© 2012 The Author(s)

About this chapter

Cite this chapter

Quiza, R., López-Armas, O., Davim, J.P. (2012). Artificial Intelligence Tools. In: Hybrid Modeling and Optimization of Manufacturing. SpringerBriefs in Applied Sciences and Technology(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28085-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28085-6_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28084-9

  • Online ISBN: 978-3-642-28085-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation