CuZn30 sac malzemenin kesme işlemlerinde kesme boşluğu etkilerinin sinir ağı ile modellenmesi - karşılaştırmalı bir çalışma

Year 2018, Volume: 11 Issue: 2, 187 - 193, 30.04.2018

Emre Çelik Onur Çavuşoğlu Hakan Gürün Nihat Öztürk

https://doi.org/10.17671/gazibtd.380961

Cited By: 3

Abstract

Clearance
effects on the product quality and blanking force in sheet metal blanking
process are initially investigated experimentally, and then modelled through
neural network (NN) approach. Using eleven different clearances from 8% to 18%
with a sampling rate of 1%, blanking processes are applied to sheet material
CuZn30 with a thickness of 1mm using a modular template. During the experiments,
blanking force, smooth sheared/fractured rate and burr height for the resulting
products are measured for each of clearance value and a certain portion of them
are taken as example patterns to train the developed feedforward NN. Several
NN-based estimation results are presented which verify that a satisfactory
neural network model is attained for the concerned parameter estimations.
Moreover, comparisons with a recent study that benefits from fuzzy logic as an
estimator tool are also presented for the same system. We realize that estimating
performance is improved using the NN and a significant contribution of our
proposal is that its design is much simpler than that of its counterpart which
requires proper and sufficient expert knowledge for tuning of characteristic
parameters such as numbers and shapes of membership functions, linguistic
control rules.

Keywords

sac metal, kesme, kesme düzlüğü, çapak boyu, kesme kuvveti, sinir ağı, bulanık mantık, tahmin

Modelling of the clearance effects in the blanking process of CuZn30 sheet metal using neural network − a comparative study

Abstract

Clearance
effects on the product quality and blanking force in sheet metal blanking
process are initially investigated experimentally, and then modelled through neural
network (NN) approach. Using eleven different clearances from 8% to 18% with a sampling
rate of 1%, blanking processes are applied to sheet material CuZn30 with a
thickness of 1mm using a modular template. During the experiments, blanking
force, smooth sheared/fractured rate and burr height for the resulting products
are measured for each of clearance value and a certain portion of them are
taken as example patterns to train the developed feedforward NN. Several NN-based
estimation results are presented which verify that a satisfactory neural
network model is attained for the concerned parameter estimations. Moreover, comparisons
with a recent study that benefits from fuzzy logic as an estimator tool are
also presented for the same system. We realize that estimating performance is improved
using the NN and a significant contribution of our proposal is that its design
is much simpler than that of its counterpart which requires proper and sufficient
expert knowledge for tuning of characteristic parameters such as numbers and
shapes of membership functions, linguistic control rules.

Keywords

sheet metal, blanking, smooth sheared, burr height, blanking force, neural network, fuzzy logic, estimation

References

• [1] 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(2-3), 249–254, 2002.
• [2] Z. Tekiner, M. Nalbant, H. Gürün, “An experimental study for the effect of different clearances on burr, smooth-sheared and blanking force on aluminium sheet metal”, Materials and Design, 27(10), 1134–1138, 2006.
• [3] D. Brokken, W.A.M. Brekelmans, F.P.T. Baaijens, “Predicting the shape of blanked products: a finite element approach”, Journal of Materials Processing Technology, 103(1), 51-56, 2000.
• [4] X. Shuqin, M. Hoogen, T. Pyttel, H. Hoffmann, “FEM simulation and experimental research on the AlMg4.5Mn0.4 sheet blanking”, Journal of Material Processing Technology, 122(2-3), 338-343, 2002.
• [5] M. Samuel, “FEM simulations and experimental analysis of parameters of influence in the blanking process”, Journal of Materials Processing Technology, 84(1-3), 97-106, 1998.
• [6] R. Hambli, F. Guerin, “Application of a neural network for optimum clearance prediction in sheet metal blanking processes”, Finite Elements in Analysis and Design, 39(11), 1039-1052, 2003.
• [7] R. Hambli, “Prediction of burr height formation in blanking processes using neural network”, International Journal of Mechanical Sciences, 44(10), 2089–2102, 2002.
• [8] G. Küçüktürk, “Experimental investigation of the effects of clearance on product quality in AA5754 sheet materıal blanking process and estiımation by fuzzy logic”, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(2), 285-294, 2016.
• [9] S. Kashid, S. Kumar, “Applications of artificial neural network to sheet metal work-a review”, American Journal of Intelligent Systems, 2(7), 168-176, 2012.
• [10] E. Çelik, H. Gör, N. Öztürk, E. Kurt, “Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator”, International Journal of Hydrogen Energy, 42(28), 17692-17699, 2017.
• [11] A. Chatterjee, A. Keyhani, “Neural network estimation of microgrid maximum solar power”, IEEE Transactions on Smart Grid, 3(4), 1860-1866, 2012.
• [12] A. Kavousi-Fard, A. Khosravi, S. Nahavandi, “A new fuzzy-based combined prediction interval for wind power forecasting”, IEEE Transactions on Power Systems, 31(1), 18-26, 2016.
• [13] E. Fantin Irudaya Raj, V. Kamaraj, “Neural network based control for switched reluctance motor drive”, International Conference on Emerging Trends in Computing, Communication and Nanotechnology, Tirunelveli India, 678-682, 2013.
• [14] O. Çavuşoglu, H. Gürün, “Investigation and fuzzy logic prediction of the effects of clearance on the blanking process of CuZn30 sheet metal”, Kovové materiály-Metallic Materials, 54(2), 125-131, 2016.
• [15] V. Özdemir, “Determination of Turkey's carbonizatıon index based on basic energy indicators by Artifıcial Neural Networks, Journal of The Faculty of Engineering and Architecture of Gazi University, 26(1), 9-15, 2011.
• [16] J. Li, J. Cheng, J. Shi, F. Huang, “Brief introduction of back propagation (BP) neural network algorithm and its improvement”, Advances in Intelligent and Soft Computing, 169, 553-558, 2012.