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

Original scientific paper

Comparison of the Technological Time Prediction Models

Goran ŠIMUNOVIĆ orcid id orcid.org/0000-0002-7159-2627 ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Slavonski Brod, Croatia
Jože BALIČ ; aculty of Mechanical Engineering University of Maribor, Maribor, Slovenia
Tomislav ŠARIĆ orcid id orcid.org/0000-0002-6339-7936 ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Slavonski Brod, Croatia
Katica ŠIMUNOVIĆ orcid id orcid.org/0000-0001-5748-7110 ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Slavonski Brod, Croatia
Roberto Lujić orcid id orcid.org/0000-0001-5123-3064 ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Slavonski Brod, Croatia
Ilija SVALINA orcid id orcid.org/0000-0003-2375-7367 ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Slavonski Brod, Croatia

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Abstract

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.

Keywords

Artificial intelligence; Neural networks; Process planning; Regression model

Hrčak ID:

56741

URI

https://hrcak.srce.hr/56741

Publication date:

30.4.2010.

Article data in other languages: croatian

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