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

Yıl 2018, , 187 - 193, 30.04.2018

Emre Çelik , Onur Çavuşoğlu , Hakan Gürün , Nihat Öztürk

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

Cited By: 3

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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