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Using artificial neural networks for modeling surface roughness of wood in machining process
Highlights Effects of machining parameters on surface roughness of wood were studied. Surface roughness decreased with increasing grit number and number of cutter. Surface roughness increased with increasing cutting depth and feed rate. Experimental results obtained were modeled by artificial neural network (ANN). It was shown that ANN can be used successfully for modeling surface roughness.
Abstract 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.
Using artificial neural networks for modeling surface roughness of wood in machining process
Highlights Effects of machining parameters on surface roughness of wood were studied. Surface roughness decreased with increasing grit number and number of cutter. Surface roughness increased with increasing cutting depth and feed rate. Experimental results obtained were modeled by artificial neural network (ANN). It was shown that ANN can be used successfully for modeling surface roughness.
Abstract 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.
Using artificial neural networks for modeling surface roughness of wood in machining process
Tiryaki, Sebahattin (author) / Malkoçoğlu, Abdulkadir (author) / Özşahin, Şükrü (author)
Construction and Building Materials ; 66 ; 329-335
2014-05-30
7 pages
Article (Journal)
Electronic Resource
English
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