Optimization of wood machining parameters using artificial neural network in CNC router

被引：5

作者：

Cakmak, Ali ^{[1
,3
]}

Malkocoglu, Abdulkadir ^{[1
]}

Ozsahin, Sukru ^{[2
]}

机构：

[1] Karadeniz Tech Univ, Fac Forestry, Dept Forest Ind Engn, Trabzon, Turkiye

[2] Karadeniz Tech Univ, Fac Forestry, Dept Ind Engn, Trabzon, Turkiye

[3] Karadeniz Tech Univ, Fac Forestry, Dept Forest Ind Engn, TR-61080 Trabzon, Turkiye

来源：

MATERIALS SCIENCE AND TECHNOLOGY | 2023年 / 39卷 / 14期

关键词：

Artificial neural network; optimal machining conditions; wood surface roughness; wood cutting power; SURFACE-ROUGHNESS PREDICTION; MEDIUM-DENSITY FIBERBOARD; EDGE-GLUED PANELS; POWER-CONSUMPTION; CUTTING FORCES; TREATED WOOD; QUALITY; MODEL; WEAR; L;

D O I：

10.1080/02670836.2023.2180901

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This study aims to determine the optimal CNC (Computer Numerical Control) machining conditions using an artificial neural network. For this purpose, Fagus orientalis, Castanea sativa, Pinus sylvestris, and Picea orientalis wood samples at 8%, 12%, and 15% moisture content (MC) were machined on a CNC router in both across and along the grain directions. Based on the experimental data of surface roughness and cutting power analyses, a total of 16 models were used. These were selected in hundreds of models that have the lowest error. The spindle speed, feed rate, and the number of cutter teeth were chosen to be different with the literature based on the length of cutter mark. As a result, optimum machining parameters were determined for each wood MC.

引用

页码：1728 / 1744

页数：17

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