Takım Yolu Ayarlarının Lif Levhanın Yüzey Pürüzlülüğü ve CNC Makinesinin Enerji Tüketimi Üzerine Etkileri

Bal, Bekir Cihad; Mengeloğlu, Fatih; Akçakaya, Elif; Gündeş, Zeynep

doi:10.17475/kastorman.1215347

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…It is seen that Ra value increases as the feed rate increases and it creates a statistically significant difference (P = 0,004). In the literature, it was stated that roughness values increase as a general tendency as the feed rate increases in the processing of wood and wood-based materials (Aykac and Sofuoglu 2021, Bal 2018, Bal and Akçakaya 2018, Davim et al 2009, Hazir et al 2018, Ilter et al 2002, Isleyen and Karamanoglu 2019, Karagoz 2010, Koc et al 2017, Pinkowski et al 2018, Sutcu and Karagoz 2012, Sofuoglu et al 2022. It seems that the data obtained in the study are compatible with the literature.…”

Section: Resultssupporting

confidence: 87%

Determination of processing characteristics of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Maderas, Cienc. tecnol.

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The main objective of this study is to determine optimum cutting parameters in order to specify the effect of densification by compressing on the processing properties of solid wood material and to achieve the best surface quality in materials densified at different rates. In line with this goal, the widely grown and low-density black poplar (Populus nigra) tree species were selected as the experimental material. Samples, which were compressed and densified by Thermo-Mechanical method at 0 %, 20 % and 40 % ratios, were processed at 1000 mm/min, 1500 mm/min and 2000 mm/min feed speeds and in 12000 rpm, 15000 rpm, 18000 rpm rotation speed on a computer numerical control machine by using two different cutters. Surface roughness values (Ra and Rz) were measured in order to evaluate surfaces obtained. Smoother surfaces were obtained in computer numerical control machining of densified samples. The lowest surface roughness values occurred in 40 % densified samples, which were the densest. The lowest surface roughness was obtained when 40 % densified samples were processed with cutter no.1 (two-flutes straight end mill), at 1000 mm/min feed speed and at 18000 rpm. Ra and Rz roughness values increased with the increase of the feed rate.

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Section: Resultssupporting

confidence: 87%

Determination of processing characteristics of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Maderas, Cienc. tecnol.

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“…Heat-treated (Ergin and Sofuoglu, 2023), surface densified (Tosun and Sofuoglu, 2023a, Tosun and Sofuoglu, 2023a studies were also carried out to determine the optimum parameters in CNC hole drilling process. Machining time (Dumanoglu and Bal, 2022) and energy consumption (Bal et al, 2022) are also invastigated. There are also review articles on the subject (Khorasani et al, 2012;Gurau and Irle, 2017).…”

Section: Introductionsmentioning

confidence: 99%

Analyze the effects of CNC machining parameters on the surface roughness (Rz) of Anatolian chestnut

Aras,

Sofuoğlu

2024

Ağaç Ve Orman

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The effect of processing parameters on the surface roughness parameter Rz (mean peak-to-valley height) in CNC machining of Anatolian chestnut (Castenia sativa Mill.) wood species used in the woodworking and furniture industry was investigated. The machining conditions were determined to obtain the lowest Rz value. The experi-ments were carried out at the speeds of 8000, 12000, and 16000 rpm and the feed rates of 1000, 1500, and 2000 mm/min with 3 and 4 machining layers by using two different cutters with a diameter of 8 mm. Rz measurements were made on the obtained surfaces using the needle scanning method according to TS EN ISO 21920-2 (2021). In the measurements perpendicular to the fibers, the lowest Rz values were obtained with cutter type 2 (the cutter of the three cutting edge straight end mill), at 16000 rpm speed, 1000/2000 mm/min feed rate, and four machining layers. In the measurements made parallel to the fibers, the lowest Rz values were obtained at the cutter type 2 (the cutter of the three cutting edge straight end mill), at the speed of 16000 rpm and the feed rate of 1000/2000 mm/min and 4th step-down. A smooth surface (lower Rz value) was obtained with fourth cuts (step down, cut layer) instead of third cuts.

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The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process

2023

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Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. In this work, an attempt was made to predict the surface roughness of the machined MDF in the milling process based on acceleration signals from an industrial piezoelectric sensor installed in the cutting zone. The surface roughness parameter Sq was adopted for the evaluation and measurement of surface roughness. The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi–Sugeno––Kang (TSK) fuzzy model with subtractive clustering. In the research, as inputs to the ANNs and fuzzy model, the kinematic parameters of the cutting process and selected measures of the acceleration signal were adopted. At the output, the values of the surface roughness parameter Sq were obtained. The results of the experiments show that the surface roughness is influenced not only by the kinematic parameters of the cutting, but also by the vibrations generated during the milling process. Therefore, by combining information on the cutting kinematics parameters and vibration, the accuracy of the surface roughness prediction in the milling process of MDF can be improved. The use of TSK fuzzy modelling based on the subtractive clustering method for integrating the information from many acceleration signal measurements in the examined range of cutting conditions meant the surface roughness was predicted with high accuracy and high reliability. With the help of two tested artificial intelligence tools, it is possible to estimate the surface roughness of the workpiece with only a small error. When using a radial neural network, the root mean square error for estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm. The surface of the sample made with the cutting parameters vc = 76 m/min and vf = 1200 mm/min is characterized by a less concentrated distribution of ordinate densities, compared to the surface of the sample cut with lower feed rates but at the same cutting speed. The most concentrated distribution of ordinate density (for the cutting speed vc = 76 m/min) is characterized by the surface, where the feed rate value was vf = 200 mm/min, with 90% of the material concentrated in the profile height of 28.2 μm. When using an RBF neural network, the RMSE of estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm.

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Takım Yolu Ayarlarının Lif Levhanın Yüzey Pürüzlülüğü ve CNC Makinesinin Enerji Tüketimi Üzerine Etkileri

Cited by 3 publications

References 18 publications

Determination of processing characteristics of wood materials densified by compressing

Determination of processing characteristics of wood materials densified by compressing

Analyze the effects of CNC machining parameters on the surface roughness (Rz) of Anatolian chestnut

The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process

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