A fuzzy logic-based model in laser-assisted bending springback control
- 60 Downloads
The present investigation deals with the proposal of a fuzzy model able to describe the inherent uncertainties related to manufacturing processes and is applied to a laser-assisted bending process. The use of such a model is aimed at controlling of the springback phenomena, which occurs during the hybrid forming process, for different set of laser process parameters, i.e., initial deflection, laser power, laser scan speed, and number of passes. In particular, the uncertainties are propagated to the residual springback by the General Transformation Method, providing only an input-output relation. The fuzzy results are then compared with the measured data leading to the evaluation of the membership level of the dataset to the uncertain model. The process maps obtained are used to select operational parameters in order to obtain a desired process output, providing as additional information how much the uncertainty of the model and the process varies by changing those operational parameters. The large variability of the process is highlighted by the fuzzy model through large band of uncertainty that occur in all the process maps generated. The fuzzy model has also been used to assess the optimal parameters in order to satisfy the requirement of the least-cost. In this case, it resulted to be convenient reduce the number of passes and use the highest laser power.
KeywordsFuzzy logic Springback Laser-assisted bending
Unable to display preview. Download preview PDF.
- 7.Guarino S, Ponticelli GS, Giannini O, Genna S, Trovalusci F (2017) Laser milling of yttria-stabilized zirconia by using a Q-switched Yb:YAG fiber laser: experimental analysis. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-017-1020-8
- 18.Akbari M, Saedodin S, Panjehpour A, Hassani M, Afrand M, Torkamany MJ (2016) Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy. Optik (Stuttg) 127(23):11161–11172. https://doi.org/10.1016/j.ijleo.2016.09.042 CrossRefGoogle Scholar
- 26.Rodger JA (2014) Application of a fuzzy feasibility Bayesian probabilistic estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets. Expert Syst Appl 41(16):7005–7022. https://doi.org/10.1016/j.eswa.2014.05.012 CrossRefGoogle Scholar
- 28.Gisario A, Barletta M, Venettacci S, Veniali F (2015) Laser-assisted bending of sharp angles with small fillet radius on stainless steel sheets: analysis of experimental set-up and processing parameters. Lasers Manuf Mater Process 2(2):57–73. https://doi.org/10.1007/s40516-015-0006-3 CrossRefGoogle Scholar
- 33.Taheri SM (2003) Trends in fuzzy statistics. Austrian J Stat 32:239–257. http://www.statistik.tuwien.ac.at/oezstat/ausg033/papers/taheri.pdf. Accessed 4 Jul 2017
- 37.Moore MJ, Kearfott RE, Cloud RB (1966) Introduction to interval analysisGoogle Scholar
- 40.Filev D, Larsson T, Lixing Ma (n.d.) Intelligent control for automotive manufacturing-rule based guided adaptation, in: 2000 26th Annu. Conf. IEEE Ind. Electron. Soc. IECON 2000. 2000 I.E. Int. Conf. Ind. Electron. Control Instrumentation. 21st Century Technol. Ind. Oppor. (Cat. No.00CH37141), IEEE, pp. 283–288. https://doi.org/10.1109/IECON.2000.973164