Abstract
This paper focusses on a study carried out in order to increase productivity in gas metal arc welding (GMAW) processes by optimising the deposition rate of the filler metal. To reach this aim, a possible solution was found in developing an adaptive system that is able to control and keep the wire feed speed constant at a desired and optimal value. This control has been accomplished by regulating an opportune variable typical of the welding process; in this case, the attention was focussed on the welding current intensity. Typical difficulties of GMAW processes, due above all to the great number of main variables and to their interdependence, suggested the possible solution by modelling a fuzzy-logic-based system, whose elements were determined by training an artificial neural network (ANN) with experimental data, obtained from bead on plate welds. At the same time, mathematical models, based on multiple regression analysis, were developed from the same data, in order to provide a comparison term and to assess the effectiveness of the neuro-fuzzy approach versus the mathematical methods. The results of this study confirmed the effectiveness of the proposed approach in the development of an integrated welding system in order to increase productivity.
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References
Ferjutz K, Davis JR, Wheaton ND (1987) ASM handbook: welding, brazing, and soldering, vol 6. American Society for Metals, Metals Park, Ohio
Cammarata S (1997) Sistemi a logica fuzzy. Etas Libri, Italia
Khanna T (1990) Foundations of neural networks. Addison-Wesley, New York
Quintino L, Ribeiro R, Faria J (1999) Classification of GMAW transfer modes using neural networks. Int J Join Mater 11(1):6–8
Li X, Simpson SW, Rados M (2000) Neural networks for online prediction of quality in gas metal arc welding. Sci Technol Weld Join 5(2):71–79
Kim IS, Park CE, Cha YH, Jae-Yeol K (2000) Use of a neural network to control bead width in GMA welding. Australas Weld J 45:33–37
Kim IS, Son JS, Park CE, Lee CW, Yarlagadda PKDV (2002) A study on prediction of bead height in robotic arc welding using a neural network. J Mater Process Technol 130–131:229–234
Bolmsjö G (1997) Knowledge based systems in robotized arc welding. Int J Join Mater 9(4):139–148
Ameling W, Borowka J, Dilthey U, Kreft L, Raus M, Scheller W (1992) Adaptive arc sensor for varying joint geometry — the use of artificial neuronal networks. Weld Cut 2:E24–E25
Eguchi K, Yamane S, Sugi H, Kubota T, Oshima K (1999) Application of neural network to arc sensor. Sci Technol Weld Join 4(6):327–334
Bae KY, Lee TH, Ahn KC (2002) An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe. J Mater Process Technol 120:458–465
Wu CS, Polte T, Rehfeldt D (2001) A fuzzy logic system for process monitoring and quality evaluation in GMAW. Weld J 80(2):33s–38s
Acknowledgements
This work was carried out with the funding of the Italian M.I.U.R. (Ministry of University and Research) and CNR (National Research Council of Italy).
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Carrino, L., Natale, U., Nele, L. et al. A neuro-fuzzy approach for increasing productivity in gas metal arc welding processes. Int J Adv Manuf Technol 32, 459–467 (2007). https://doi.org/10.1007/s00170-005-0360-y
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DOI: https://doi.org/10.1007/s00170-005-0360-y