Weight losses estimation of industrial punch tools

Keywords: Industrial punch, ANN, weight losses, punch diameter, punch stroke, stroke noise

Abstract

Many studies have been conducted on the estimation of weight losses of industrial tools; however, these investigations are very rare. And there is no prediction study on the weight loss of industrial punches. An artificial neural network model (ANN) was proposed in order to establish relationships with the field data including input parameters as punch diameter, punch stroke, stroke noise, and punch temperature and output parameter as weight loss of punch. Effect of each parameter on the weight loss of industrial punch was analyzed with the developed model. An empirical formula was also obtained with the generalization capabilities of the ANN system. Analysis results showed that the estimation results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural designs for real-life industrial punch estimation applications.

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References

References
[1] K.Lawanwong, N. Pornputsiri, G.Luangsopapun, An investigation of adhesion wear behavior of tool steel on blanking die, 2011 International Conference on Advanced Materials Engineering; Singapore, 15(2011)25-30.
[2] P.V.D. Marcondes, A.M. Eto, P.A.C. Beltrǎo, P.C. Borges, A smart stamping tool for punching and broaching combination, Journal of Materials Processing Technology,206(2008) 184–193.
[3] N. Hatanaka, K. Yamaguchi, N. Takakura, T. Iizuka, Simulation of sheared edge formation process in blanking of sheet metals, Journal of Materials Processing Technology, 140(2003) 628–634.
[4] Y. Arslan, A. Özdemir, Punch structure, punch wear and cut profiles of AISI 304 stainless steel sheet blanks manufactured using cryogenically treated AISI D3 tool steel punches. The International Journal of Advanced Manufacturing Technology 87.1-4 (2016): 587-599.
[5] H. Makich, L. Carpentier, G. Monteil, X. Roizard, J. Chambert, P. Picart, Metrology of the burr amount-correlation with blanking operation parameters (blanked material-wear of the punch),Int. J. Mater Form © Springer/ ESAFORM, Suppl 1 (2008) 1243- 1246.
[6] X.Wu, H. Bahmanpour, K. Schmid, Characterization of mechanically sheared edges of dual phase steels, Journal of Materials Processing Technology, 212(2012) 1209–1224.
[7] J.J. Hern´andez, P. Franco, M. Estrems, F. Fuara, Modelling and experimental analysis of the effects of tool wear on form errors in stainless steel blanking, Journal of Materials Processing Technology, 180 (2006) 143–150.
[8] T.Z. Quazi, R. Shaikh, A. Totre, R. Nishad, S. Bodke, A. Chauhan, Blanking process optimization using taguchi method, International Journal of Engineering Research and Development, 7 (2013) 45-51.
[9] R. Hambli, Blanking tool wear modeling using the finite element method, International Journal of Machine Tools & Manufacture, 41 (2001) 1815–1829.
[10] M.Y. Myint,J.Y.H. Fuh, Y.S. Wong, L. Lu, Z.D. Chen, C.M. Choy, Evaluation of wear mechanisms of Y-TZP and tungsten carbide punches, Journal of Materials Processing Technology, 140(2003) 460–464.
[11] Z. Tekiner, M. Nalbant, H. Gürün, An experimental study for the effect of different clearances on burr, smooth-sheared and blanking force on aluminum sheet metal, Materials and Design, 27 (2006) 1134–1138.
[12] G. Fang, P. Zeng, L. Lou, Finite element simulation of the effect of clearance on the forming quality in the blanking process, Journal of Materials Processing Technology, 122(2002) 249-254.
[13] S.Y. Lou, Effect of the geometry and the surface treatment of punching tools on the tool life and wear conditions in the piercing of thick steel plate, Journal of Materials Processing Technology, 88 (1999) 122-133.
[14] F. Faura, A. García, M. Estrems, Finite element analysis of optimum clearance in the blanking process, Journal of Materials Processing Technology,80-81 (1998) 121-125.
[15] T., S. Kwak, Y.,J. Kim, W.B. Bae, Finite element analysis on the effect of die clearance on shear planes in fine blanking, Journal of Materials Processing Technology, 130-131(2002) 462-468.
[16] D. Mohan S. Renganarayanan, A. Kalanidhi, Cryogenic treatment to augment wear resistance of tool and die steels, Cryogenics, 41 (2001) 149-155.
[17] Yıldızel, S. A., & Öztürk, A. U. (2016). A study on the estimation of prefabricated glass fiber reinforced concrete panel strength values with an artificial neural network model. CMC: Computers, Materials & Continua, 52(1), 42-51.
[18] Uygur, I., Cicek, A., Toklu, E., Kara, R., & Saridemir, S. (2014). Fatigue life predictions of metal matrix composites using artificial neural networks. Archives of Metallurgy and Materials, 59(1), 97-103.
[19] Y. Khademi, F., Akbari, M., & Jamal, S. M. (2016). Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling. Revista Romana de Materiale-Romanian Journal of Materials, 46(3), 343- 350
[20] Uygur I, Gerengi H, Arslan Y, Kurtay M. The Effects of Cryogenic Treatment on the Corrosion of AISI D3 Steel. Materials Research. 2015;18(3):569-574.
[21] Ramos, Leandro Brunholi, et al. Tribocorrosion and electrochemical behavior of DIN 1.4110 martensitic stainless steels after cryogenic heat treatment. Materials Research, 2017, 20.2:460-468.
[22] Arslan, Y., Uygur, I., & Jazdzewska, A. (2015). The effect of cryogenic treatment on microstructure and mechanical response of AISI D3 tool steel punches. Journal of Manufacturing Science and Engineering, 137(3), 034501.
[23] ASM Handbook Volume 1 Desk Edition, Properties and Selection Irons Steels and High-Performance Alloys, Ohio; 2002. p. 755-866.
Published
2019-09-30
How to Cite
[1]
Y. Arslan, “Weight losses estimation of industrial punch tools”, IJISAE, vol. 7, no. 3, pp. 183-187, Sep. 2019.
Section
Research Article