ANN Prediction of Laser Power, Cutting Speed, and Number of Cut Annual Rings and Their Influence on Selected Cutting Characteristics of Spruce Wood for CO2 Laser Processing

被引:1
作者
Ruziak, Ivan [1 ]
Igaz, Rastislav [1 ]
Kubovsky, Ivan [1 ]
Tudor, Eugenia Mariana [2 ,3 ]
Gajtanska, Milada [4 ]
Jankech, Andrej [4 ]
机构
[1] Tech Univ Zvolen, Fac Wood Sci & Technol, Dept Phys Elect Engn & Appl Mech, TG Masaryka 24, Zvolen 96001, Slovakia
[2] Salzburg Univ Appl Sci, Green Engn & Circular Design Dept, Markt 136a, A-5431 Kuchl, Austria
[3] Transilvania Univ Brasov, Fac Furniture Design & Wood Engn, B Dul Eroilor 29, Brasov 500036, Romania
[4] Tech Univ Zvolen, Fac Wood Sci & Technol, Dept Math & Descript Geometry, TG Masaryka 24, Zvolen 96001, Slovakia
关键词
CO2; laser; artificial neural networks; spruce wood; cutting kerf; heat-affected zone; sensitivity analysis; ARTIFICIAL NEURAL-NETWORKS; SURFACE-ROUGHNESS; BONDING STRENGTH; PARAMETERS; CONSUMPTION;
D O I
10.3390/ma17133333
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In this work, we focus on the prediction of the influence of CO2 laser parameters on the kerf properties of cut spruce wood. Laser kerf cutting is mainly characterized by the width of kerf and the width of the heat-affected zone, which depend on the laser power, cutting speed, and structure of the cut wood, represented by the number of cut annual rings. According to the measurement results and ANN prediction results, for lower values of the laser power (P) and cutting speed (v), the effect of annual rings (ARs) is non-negligible. The results of the sensitivity analysis show that the effect of v increases at higher energy density (E) values. With P in the range between 100 and 500 W, v values between 3 and 50 mm<middle dot>s(-1), and AR numbers between 3 and 11, the combination of P = 200 W and v = 50 mm<middle dot>s(-1), regardless of the AR value, leads to the best cut quality for spruce wood. In this paper, the main goal is to show how changes in the input parameters affect the characteristics of the cutting kerf and heat-affected zones for all possible input parameter values.
引用
收藏
页数:22
相关论文
共 28 条
[1]   Numerical simulation of metal removal in laser drilling using radial point interpolation method [J].
Abidou, Diaa ;
Yusoff, Nukman ;
Nazri, Nik ;
Awang, M. A. Omar ;
Hassan, Mohsen A. ;
Sarhan, Ahmed A. D. .
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS, 2017, 77 :89-96
[2]  
Akyüz I, 2019, WOOD RES-SLOVAKIA, V64, P483
[3]   An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process [J].
Akyuz, Ilker ;
Ozsahin, Sukru ;
Tiryaki, Sebahattin ;
Aydin, Aytac .
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2017, 19 (05) :1449-1458
[4]  
Asibu E.K., 2009, Principles of Laser Materials Processing
[5]  
Avramidis S, 2005, WOOD FIBER SCI, V37, P682
[6]   Characterisation and modification of the heat affected zone during laser material processing of wood and wood composites [J].
Barcikowski, S ;
Koch, G ;
Odermatt, J .
HOLZ ALS ROH-UND WERKSTOFF, 2006, 64 (02) :94-103
[7]   Predictive Performance of Artificial Neural Network and Multiple Linear Regression Models in Predicting Adhesive Bonding Strength of Wood [J].
Bardak, S. ;
Tiryaki, S. ;
Bardak, T. ;
Aydin, A. .
STRENGTH OF MATERIALS, 2016, 48 (06) :811-824
[8]   Investigation and neural network prediction of wood bonding quality based on pressing conditions [J].
Bardak, Selahattin ;
Tiryaki, Sebahattin ;
Nemli, Gokay ;
Aydin, Aytac .
INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES, 2016, 68 :115-123
[9]   Determination of the surface characteristics of medium density fibreboard processed with CNC machine and optimisation of CNC process parameters by using artificial neural network [J].
Demir, Aydin ;
Birinci, Abdullah Ugur ;
Ozturk, Hasan .
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2021, 35 :929-942
[10]   Determination of CNC processing parameters for the best wood surface quality via artificial neural network [J].
Demir, Aydin ;
Cakiroglu, Evren Osman ;
Aydin, Ismail .
WOOD MATERIAL SCIENCE & ENGINEERING, 2022, 17 (06) :685-692