Using an Adaptive Network-based Fuzzy Inference System to Estimate the Vertical Force in Single Point Incremental Forming
Keywords:
adaptive network-based fuzzy inference system, CNC milling machines, incremental forming, technological forceAbstract
Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of technological parameters (diameter of the tool, feed and incremental step) as inputs. Sets of experimental data were generated and processed by means of the proposed system, to make use of the learning ability of it to extract the empirical values of the technological force from rough data.References
Aerens, R.; Eyckens, P.; Van Bael, A.; Duflou, J. R. (2010); Force prediction for single point incremental forming deduced from experimental and FEM observations, International Journal of Advanced Manufacturing Technology, 46(9-12), 969-982, 2010. https://doi.org/10.1007/s00170-009-2160-2
Behera, A.K.; De Sousa R.A.; Ingarao, G.; Oleksik, V. (2017); Single point incremental forming: An assessment of the progress and technology trends from 2005 to 2015, Journal of Manufacturing Processes, 27, 37-62, 2017. https://doi.org/10.1016/j.jmapro.2017.03.014
Breaz, R.; Bologa, O.; Tera, M.; Racz, G. (2013); Determination of Technological Forces in the Incremental Forming Process, Applied Mechanics and Materials, 371, 133-137, 2013.
Caydas, U.; Hascalik, A.; Ekici, S. (2009); An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM, Expert Systems with Applications, 36, 6135-6139, 2009. https://doi.org/10.1016/j.eswa.2008.07.019
Ceretti, E.; Giardini, C.; Attanasio, A. (2004); Experimental and simulative results in sheet incremental forming on CNC machines, Journal of Materials Processing Technology, 152(2), 176-184, 2004. https://doi.org/10.1016/j.jmatprotec.2004.03.024
Ciupan, E.; Lungu, F.; Ciupan, C. (2015); ANN Training Method with a Small Number of Examples Used for Robots Control, International Journal of Computers Communications & Control, 10(5), 643-653, 2015. https://doi.org/10.15837/ijccc.2015.5.2027
Duflou, J. R.; Szekeres, A.; Vanherck, P. (2005); Force Measurements for Single Point Incremental Forming: An Experimental Study, Advanced Materials Research, 6-8, 441-448, 2005.
Dzitac, I.; Filip, F.G.; Manolescu, M.J. (2017); Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, 12(6), 748-789, 2017. https://doi.org/10.15837/ijccc.2017.6.3111
Dzitac, I. (2015); The fuzzification of classical structures: A general view, International Journal of Computers Communications & Control, 10(6), 772-788, 2015. https://doi.org/10.15837/ijccc.2015.6.2069
Gatea, S.; Ou, H.; Mccartney, G. (2016); Review on the influence of process parameters in incremental sheet forming, International Journal of Advanced Manufacturing Technology, 87, 479-499, 2016. https://doi.org/10.1007/s00170-016-8426-6
Haidegger, T.; Kovacs, L.; Precup, R.-E.; Benyo, B.; Benyo, Z.; Preitl, S. (2012); Simulation and control for telerobots in space medicine, Acta Astronautica, 181(1), 390-402, 2012.
Herrera-Viedma, E.; Lopez-Herrera, A.G. (2010); A review on information accessing systems based on fuzzy linguistic modelling, International Journal of Computational Intelligence Systems, 3(4), 420-437, 2010. https://doi.org/10.1080/18756891.2010.9727711
Hyacinth Suganthi, X.; Natarajan, U.; Sathiyamurthy, S.; Chidambaram, K. (2013); Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model, International Journal of Advanced Manufacturing Technology, 68, 339-347, 2013. https://doi.org/10.1007/s00170-013-4731-5
Jeswiet, J.; Micari, F.; Hirt, G.; Bramley A.; Duflou, J.; Allwood, J.(2005); Asymmetric Single Point Incremental Forming of Sheet Metal, Annals of CIRP, 54, 623-650, 2005.
Jiao, Y.; Lei, S.; Pei, Z.J.; Lee, E.S. (2004); Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations, International Journal of Machine Tools and Manufacture, 44(15), 1643-1651, 2004. https://doi.org/10.1016/j.ijmachtools.2004.06.004
Kumanan, S.; Jesuthanam, C. P. (2008); Ashok Kumar, R.; Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness, International Journal of Advanced Manufacturing Technology, 35, 778-788, 2008. https://doi.org/10.1007/s00170-006-0755-4
Li, Y.; Liu, Z.; Lu, H.; Daniel, W. J. T.; Liu, S.; Meehan, P. A. (2014); Efficient force prediction for incremental sheet forming and experimental validation, International Journal of Advanced Manufacturing Technology, 73, 571-587, 2014. https://doi.org/10.1007/s00170-014-5665-2
Micari, F.; Ambrogio, G.; Filice, L. (2007); Shape and dimensional accuracy in single point incremental forming: State of the art and future trends, Journal of Materials Processing Technology, 191(1-3), 390-395, 2007. https://doi.org/10.1016/j.jmatprotec.2007.03.066
Oprea, M.; Mihalache, S. F.; Popescu, M. (2017); Computational Intelligence-based PM2.5 Air Pollution Forecasting, International Journal of Computers Communications & Control, 12(3), 365-380, 2017. https://doi.org/10.15837/ijccc.2017.3.2907
Perez-Santiago, R.; Bagudanch FrigolA and I.; Garcia-Romeu de Luna, M.L. (2011); Force Modeling in Single Point Incremental Forming of Variable Wall Angle Components, Key Engineering Materials, 473, 833-840, 2011. https://doi.org/10.4028/www.scientific.net/KEM.473.833
Precup, R.-E.; Hellendoorn, H. (2011); A survey on industrial applications of fuzzy control, Computers in Industry, 62, 213-226, 2011. https://doi.org/10.1016/j.compind.2010.10.001
Salahshoor, K.; Kordestani, M.; Khoshro, M.S. (2010); XFault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers, Energy, 35, 5472-5482, 2010. https://doi.org/10.1016/j.energy.2010.06.001
Schafer, T.; Schraft, R.D. (2005); Incremental sheet metal forming by industrial robots, Rapid Prototyping Journal, 11(5), 278-286, 2005. https://doi.org/10.1108/13552540510623585
Tera, M.; Breaz, R.E.; Bologa, O.; Racz, S.G.(2015); Developing a Knowledge Base about the Technological Forces within the Asymmetric Incremental Forming Process, Key Engineering Materials, 651, 1115-1121, 2015.
Tseng, T.-L.; Konada, U.; Kwon, Y. (2016); A novel approach to predict surface roughness in machining operations using fuzzy set theory, Journal of Computational Design and Engineering, 3, 1-13, 2016. https://doi.org/10.1016/j.jcde.2015.04.002
Velosa De Sena, J.I. (2015); Advanced numerical framework to simulate Incremental Forming Processes, Ph.D. Thesis, University of Aveiro, Portugal, 2015.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
