The influence of machining parameters on surface roughness of MDF in milling operation

İşleyen, Ümmü Karagöz; Karamanoğlu, Mehmet

doi:10.15376/biores.14.2.3266-3277

Cited by 22 publications

(11 citation statements)

References 26 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: 86%

“…While there was a slight decrease in the Ra value with the increase of speed from 12000 rpm to 15000 rpm, the decrease was higher with the increase of the speed from 15000 rpm to 18000 rpm. With the increase in the speed in rotary cutting cutters, roughness values decrease and smoother surfaces can be obtained (Aykac and Sofuoglu 2021, Davim et al 2009, Hazir et al 2018, Isleyen and Karamanoglu 2019, Karagoz 2010, Koc et al 2017, Sofuoglu 2015, Sutcu and Karagoz 2012, Sutcu and Karagoz 2013. It is seen that the data obtained in the study show similar trends with the literature.…”

Section: Resultssupporting

confidence: 80%

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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: 86%

Section: Resultssupporting

confidence: 80%

Determination of processing characteristics of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Maderas, Cienc. tecnol.

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“…In another study [2], a feed rate of 2 m/min and a rotation speed of 18,000 rpm were found to be the optimal parameters for achieving superior surface quality in beech wood processing.…”

Section: Introductionmentioning

confidence: 96%

Evaluation of CNC Routed Surface Quality of Beech (Fagus sylvatica L.) with Different Milling Angles as Function of Grain Orientation

Lungu,

Gurau,

Cosereanu

2023

FWIAFE

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The paper presents an experimental investigation of the quality of CNC routed surfaces with a V-Grooving router bit (90°) on various milling angles as a function of wood grain direction on beech (Fagus sylvatica L.) wood. The quality of the machined surface was first visually assessed for the presence of raised fibre and other processing defects on the routed circles with a diameter of 180 mm using feed speeds of 3 m/min and 6 m/min respectively, and spindle speeds of 12,000, 15,000, and 18,000 rpm. The quality of the processed surfaces on the circle indicated the most unfavourable CNC routing regime accompanied by the conclusions regarding the most unfavourable angles related to the grain. Further research considered the assessment of the surface quality on straight CNC routing with angles of 0, 15, 30, 45, 60, 75, and 90° related to the wood grain direction at feed speeds of 3 and 6 m/min and spindle speed of 15,000 rpm. In this case, the characterisation of the wood surface quality was made by measuring the roughness parameters. The highest values of the roughness parameters, showing the presence of the fibres detached from the surface, were recorded for the cutting angle of 60, followed closely by 75°, then 45°, and 90° at a feed speed of 3 m/min. No significant difference was found between the Rk of the cutting angles from 0° to 45°, but it increased significantly for 60, 75, and 90°, showing frequent deeper anatomical voids on the surface.

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“…Many scientific works are focused on the determination of the influence of cutting parameters on the surface roughness of MDF in the machining process. However, the optimal parameters in these studies were generally found using the Taguchi method and statistical methods [31]. Artificial neural networks have found wide application in wood science, e.g., in recognizing wood species [32], wood-drying process [33], predicting some mechanical properties of wood [34], optimizing process parameters in the manufacturing process of wood products [35], classification of wood and veneer defects [36], calculation of thermal conductivity of wood [37], analysis of moisture in wood [38] and prediction of wood cracking resistance [39].…”

Section: Introductionmentioning

confidence: 99%

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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The influence of machining parameters on surface roughness of MDF in milling operation

Cited by 22 publications

References 26 publications

Determination of processing characteristics of wood materials densified by compressing

Determination of processing characteristics of wood materials densified by compressing

Evaluation of CNC Routed Surface Quality of Beech (Fagus sylvatica L.) with Different Milling Angles as Function of Grain Orientation

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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