Using artificial neural networks for modeling surface roughness of wood in machining process

Autor:	Sebahattin Tiryaki, Şükrü Özşahin, Abdulkadir Malkoçoğlu
Rok vydání:	2014
Předmět:	Machining process Materials science Materials processing Artificial neural network business.industry Building and Construction Solid wood Grain size Surface roughness General Materials Science Composite material Wood industry Process engineering business Civil and Structural Engineering
Zdroj:	Construction and Building Materials. 66:329-335
ISSN:	0950-0618
DOI:	10.1016/j.conbuildmat.2014.05.098
Popis:	Surface quality of solid wood is very important for its effective utilization in further manufacturing processes. In this study, the effects of wood species, feed rate, number of cutter, cutting depth, wood zone (earlywood–latewood) and grain size of abrasives on surface roughness were investigated and modeled by artificial neural networks. It was shown that the artificial neural network prediction model obtained is a useful, reliable and quite effective tool for modeling surface roughness of wood. Thus, the results of the present research can be successfully applied in the wood industry to reduce the time, energy and high experimental costs.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ecb702ca8e783b2b6e93d4396e345eab https://doi.org/10.1016/j.conbuildmat.2014.05.098 Zobrazit plný text záznamu Full Text from ScienceDirect