Prediction of Surface Roughness Using a Feed-Forward Neural Network

• Autorzy: Jernej Senveter , Simon Klancnik , Joze Balic , Franc Cus
• Języki publikacji: EN

Abstrakty

EN

This article presents the development of a system for predicting surface roughness, using a feed-forward neural network. The primary goal was to develop a system in order to predict with complex reliability and defined accuracy. However, this system is designed in such a way that it is also possible to use it for various other workpieces. The described system uses a neural network which receives signals at the input level. The signals then travel through all hidden levels to the output level, where the responses to input signals are received. Data are used which affects the selection of surface roughness regarding the input to the neural network. Three different inputs in total are used for the neural network. Data which represents the inputs to the neural network are encoded, so that they occupy values between 0 and 1. Adequate cutting speed, feed, and depth of cut, are selected in order to achieve an adequate surface roughness of the workpiece, using the trained neural network. This contributes to the optimisation and economy of machining, which is very important during the production of an individual product and also for an individual company or organisation when transferring the final product to the contracting authority or final customer. (original abstract)

Słowa kluczowe

EN

Neural networks; Optimalization

PL

Sieci neuronowe; Optymalizacja

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2010
• Tom: 1
• Numer: nr 2
• Strony: 47--55

Twórcy

autor

Jernej Senveter

• University of Maribor, Slovenia

autor

Simon Klancnik

• University of Maribor, Slovenia

autor

Joze Balic

• University of Maribor, Slovenia

autor

Franc Cus

• University of Maribor, Slovenia

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