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Strojarstvo : časopis za teoriju i praksu u strojarstvu, Vol. 52 No. 2, 2010.

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Comparison of the Technological Time Prediction Models

Goran ŠIMUNOVIĆ orcid id orcid.org/0000-0002-7159-2627 ; Strojarski fakultet u Slavonskom Brodu, Sveučilište J. J. Strossmayera u Osijeku, Slavonski Brod, Hrvatska
Jože BALIČ ; Fakulteta za strojništvo Univerze v Mariboru, Maribor, Slovenia
Tomislav ŠARIĆ orcid id orcid.org/0000-0002-6339-7936 ; Strojarski fakultet u Slavonskom Brodu, Sveučilište J. J. Strossmayera u Osijeku, Slavonski Brod, Hrvatska
Katica ŠIMUNOVIĆ orcid id orcid.org/0000-0001-5748-7110 ; Strojarski fakultet u Slavonskom Brodu, Sveučilište J. J. Strossmayera u Osijeku, Slavonski Brod, Hrvatska
Roberto Lujić orcid id orcid.org/0000-0001-5123-3064 ; Strojarski fakultet u Slavonskom Brodu, Sveučilište J. J. Strossmayera u Osijeku, Slavonski Brod, Hrvatska
Ilija SVALINA orcid id orcid.org/0000-0003-2375-7367 ; Strojarski fakultet u Slavonskom Brodu, Sveučilište J. J. Strossmayera u Osijeku, Slavonski Brod, Hrvatska

Puni tekst: engleski pdf 1.876 Kb

str. 137-145

preuzimanja: 765

citiraj

Sažetak

The paper sets out to describe the results obtained by investigating the
prediction of technological parameters and, indirectly, of technological
time needed for seam tube polishing. The results of experimental
investigations were used to define, analyse and compare two models. One
is a mathematical i.e. statistical model obtained by the application of the
least squares method and the least absolute deviation method. The other is
a model based on the application of neural networks. To define the model
based on the application of neural networks various structures of the back-
propagation neural network with one hidden layer were analysed and the
optimal one with the minimum RMS error was selected.
The more precise predictions of technological time provided by the
models supplement the previously defined manufacturing operations,
replace the predictions based on the technologists’ experience and form
the basis on which to plan production and control delivery times. The
work of technologists is thus made easier and the production preparation
technological time shorter.

Ključne riječi

Artificial intelligence; Neural networks; Process planning; Regression model

Hrčak ID:

56741

URI

https://hrcak.srce.hr/56741

Datum izdavanja:

30.4.2010.

Podaci na drugim jezicima: hrvatski

Posjeta: 2.255 *