Naslov
Prediction of surface roughness with genetic programming

Autori
Matko Glučina, Ammar Muminović

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

Izvornik
Ri-STEM-2021 Proceedings / Lorencin, Ivan ; Baressi Šegota, Sandi ; Car, Zlatan - , 2021, 71-76

Skup
International Student Scientific Conference (Ri-STEM 2021)

Mjesto i datum
Rijeka, Hrvatska, 10.06.2021. - 11.06.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial intelligence, Face mask, Machine Learning, Multi-Layer Perceptron

Sažetak
This paper represents the prediction of surface roughness after using an electrical machine. Using the given data set which is consisted of four parameters the depth of cut, feed rate, cutting speed, and surface roughness. The goal was to achieve a mathematical equation that can describe the regression function of trained and tested parameters. Python script consists of many libraries such as pandas, matplotlib, but most important are GP learn genetics which has implemented Symbolic Regression and sk learn which has tools to check R2 score that is coefficient of determination and RMSE- Root Mean square Error. The regression model can be a well-established method for data analysis in various field applications.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

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