Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03139207" target="_blank" >RIV/68407700:21230/08:03139207 - isvavai.cz</a>

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Modeling Tool Wear in End Milling Using Enhanced GMDH Learning Networks

Original language description

This paper presents an enhanced approach to predictive modeling for determining tool wear in end milling operations based on enhanced group method of data handling (e GMDH). Using milling input parameters (speed, feed, and depth of cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possibleto predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used

Czech name

Modelování opotřebení vrtáků pomocí eGMDH algoritmu

Czech description

Článek prezentuje přístup využívající algrotmu eGMDH k predikci opotřebení vrtných nástrojů při vrtání. To je odhadováno ze vstupních parametrů - rychlost, příkon a hloubka vrtu. V článku je prezentována jak metoda eGMDH tak i výsledky realizovaných experimentů.

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JC - Computer hardware and software

OECD FORD branch

—

Project

<a href="/en/project/1ET101210513" target="_blank" >1ET101210513: Relational machine learning for analysis of biomedical data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2008

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

The International Journal of Advanced Manufacturing Technology

ISSN

0268-3768

e-ISSN

—

Volume of the periodical

39

Issue of the periodical within the volume

11-12

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

—

UT code for WoS article

000260699300003

EID of the result in the Scopus database

—