[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Multiobjective optimization of torch brazing process by a hybrid of fuzzy logic and multiobjective artificial bee colony algorithm

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This paper describes an application of a hybrid of fuzzy logic (FL) and multiobjective artificial bee colony algorithm (MOABC) for optimizing the torch brazing process of aluminum in the fabrication of condensers in the automotive manufacturing industry of Juarez, Mexico. This work aims to show how artificial intelligence is being applied in the manufacturing sector of Mexico for optimizing processes leading to cost reduction. The approach consists of using FL as surrogate model of the brazing process; after, MOABC is applied to find the nondominated solutions for leak rate which is a quality test of the condenser and production time. Results show the use of artificial intelligence is an excellent tool for optimizing manufacturing processes leading to improve productivity, mainly in the selected region, where this type of methodologies are fairly new in applicability.

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
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ajorlou, S., & Shams, I. (2013). Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment. Journal of Intelligent Manufacturing, 24, 1145–1156.

    Article  Google Scholar 

  • Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23, 1001–1014.

    Article  Google Scholar 

  • Bandyopadhyay, S., & Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24, 707–716.

    Article  Google Scholar 

  • Brajevic, I., & Tuba, M. (2013). An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24, 729–740.

    Article  Google Scholar 

  • Chaube, A., Benyoucef, L., & Tiwari, M. K. (2012). An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. Journal of Intelligent Manufacturing, 23, 1141–1155.

    Article  Google Scholar 

  • Deb, K., Pratao, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • Edwin Raja Dhas, J., & Kumanan, S. (2011). Optimization of parameters of submerged arc weld using non-conventional techniques. Applied Soft Computing, 11(8), 5198–5204.

    Article  Google Scholar 

  • Hiremath, N. C., Sahu, S., & Tiwari, M. K. (2013). Multiobjective outbound logistics network design for a manufacturing supply chain. Journal of Intelligent Manufacturing, 24, 1071–1084.

    Article  Google Scholar 

  • Hou, T. H., Su, C. H., & Chang, H. Z. (2008). An integrated multiobjective immune algorithm for optimizing the wire bonding process of integrated circuits. Journal of Intelligent Manufacturing, 19, 361–374.

    Article  Google Scholar 

  • Humpston, G., & Jacobson, D. (2004). Principles of soldering. Materials Park, OH: ASM International.

  • INEGI. (2010). National Institute for Statistics, Geography and Informatics. Mexico: Secretary of Economy.

    Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.

  • Katherasan, D., Elias, J. V., Sathiya, P., & Noorul Haq, A. (2012). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0675-0.

  • Kim, I. S., Jeong, Y. J., Lee, C. W., & Yarlagadda, P. (2003). Prediction of welding parameters for pipeline welding using an intelligent system. International Journal of Advanced Manufacturing Technology, 22(9), 713–719.

    Article  Google Scholar 

  • Li, J. Q., Pan, Q. K., & Gao, K. Z. (2011). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. International Journal of Advanced Manufacturing Technology, 55(9–12), 1159–1169.

    Article  Google Scholar 

  • Li, M., Wang, L., & Wu, M. (2013). A multiobjective genetic algorithm approach for solving feature addition problem in feature fatigue analysis. Journal of Intelligent Manufacturing, 24, 1197–1211.

    Article  Google Scholar 

  • Lin, H. L. (2012). Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi-Neural-Genetic Approach. Journal of Intelligent Manufacturing, 23(3), 679–686.

    Article  Google Scholar 

  • Liukkonen, M., Havia, E., Leinonen, H., & Hiltunen, Y. (2011). Quality-oriented optimization of wave soldering process by using self-organizing maps. Applied Soft Computing, 11(1), 214–220.

    Article  Google Scholar 

  • Nasiraghdam, H., & Jadid, S. (2012). Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multiobjective artificial bee colony (MOABC) algorithm. Solar Energy, 86(10), 3057–3071.

    Article  Google Scholar 

  • Omkar, S. N., Senthilnath, J., Khandelwal, R., Narayana Naik, G., & Gopalakrishnan, S. (2011). Artificial Bee Colony (ABC) for multiobjective design optimization of composite structures. Applied Soft Computing, 11(1), 489–499.

    Article  Google Scholar 

  • Ozturk, A., Cobanli, S., Erdosmus, P., & Tosun, S. (2010). Reactive power optimization with artificial bee colony algorithm. Scientific Research and Essays, 5(19), 2848–2857.

    Google Scholar 

  • Prasad, N. R., Nguyen, H. T., Walker, E. A., & Walker, C. L. (2003). A first course in fuzzy and neural control. Boca Raton, Florida: Chapman and Hall.

    Google Scholar 

  • Reza Pasandideh, S. H., & Akhavan Niaki, S. T. (2013). A multiobjective facility location model with batch arrivals: Two parameter-tuned- meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24, 331–348.

    Article  Google Scholar 

  • Rubio-Largo, A., Vega-Rodriguez, M. A., Gomez-Pulido, J. A., & Sanchez-Perez, J. M. (2013). A multiobjective approach base don artificial bee colony for the static routing and wavelength assingment problema. Soft computing, 17(2), 199–211.

    Article  Google Scholar 

  • Tseng, H. Y. (2006). Welding parameters optimization for economic design using neural approximation and genetic algorithm. International Journal of Advanced Manufacturing Technology, 27(9–10), 897–901.

    Article  Google Scholar 

  • Wang, Y. Q., Afsar, A. M., & Song, J. (2009). Optimization of brazing conditions for OFHC Cu and ASTM A501 low carbon steel by Taguchi method. International Journal of Precision Engineering and Manufacturing, 10(3), 97–104.

    Article  Google Scholar 

  • Yang, L., Deuse, J., & Jiang, P. (2013). Multiobjective optimization of facility planning for energy intensive companies. Journal of Intelligent Manufacturing, 24, 1095–1109.

    Article  Google Scholar 

  • Zacharla, P Th, & Nearchou, A. C. (2012). Multiobjective fuzzy assembly line balancing using genetic algorithms. Journal of Intelligent Manufacturing, 23, 615–627.

  • Zhang, H., Zhu, Y., Zou, W., & Yan, X. (2012). A hybrid multiobjective artificial bee colony algorithm for burdening optimization of copper strip production. Applied Mathematical Modelling, 36(6), 2578–2591.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, Autonomous University of Ciudad Juarez, Ave. Del Charro 450 Norte, 32315 , Ciudad Juarez, Chihuahua, Mexico

    Alejandro Alvarado-Iniesta, Jorge L. García-Alcaraz, Manuel Piña-Monarrez & Luis Pérez-Domínguez

Authors
  1. Alejandro Alvarado-Iniesta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge L. García-Alcaraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manuel Piña-Monarrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luis Pérez-Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Alvarado-Iniesta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alvarado-Iniesta, A., García-Alcaraz, J.L., Piña-Monarrez, M. et al. Multiobjective optimization of torch brazing process by a hybrid of fuzzy logic and multiobjective artificial bee colony algorithm. J Intell Manuf 27, 631–638 (2016). https://doi.org/10.1007/s10845-014-0899-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0899-2

Keywords

Search

Navigation