Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

Ayyıldız, Mustafa

doi:10.1108/sr-02-2019-0051

Cited by 8 publications

(4 citation statements)

References 50 publications

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“…For this purpose, a confirmation experiment by using the Taguchi optimization technique was performed. Equation (6) was used in order to find the optimum surface roughness [30]: (6) where (A 1 , B 3 , and C 1 ) show the optimal-level mean values of surface roughness (Ra opt ) in Table 2 and T Ra declares the 7. According to these equations, the minimum surface roughness can be found as 2.022 μm.…”

Section: Optimization Of Surface Roughnessmentioning

confidence: 99%

“…Diverse machining processes such as drilling, milling, and turning are applied to obtain the required size and form of the MDF [1][2][3][4]. e most common technique for examining machining efficiency is based on the surface finishing and covers visual evaluation of surface quality measurement [5,6]. Nowadays, especially, algorithms based on experimental design are used to optimize various engineering problems [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

Ayyıldız

Kara

2021

Advances in Materials Science and Engineering

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This study focuses on the examination of the effect of cutting parameters on surface roughness when drilling medium-density fiberboard (MDF) with a parallel robot. Taguchi technique was applied to find the optimum drilling parameters and, later, the drilling processing. Experimental layout was established using the Taguchi L18 orthogonal array, and experimental data were examined via a statistical analysis of variance (ANOVA). Experimental results were performed by multiple regression analysis (linear and quadratic). Correlation coefficient (R2) was found 99.46% for surface roughness with the quadratic regression model. By the Taguchi analysis, the optimum values for the surface roughness were found to be a point angle of 118°, a cutting speed of 47.1 m/min, and a feed rate of 0.01 mm/rev. The optimization outcomes presented that the Taguchi technique had been successfully performed to decide the optimal surface roughness of the MDF in the drilling.

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Section: Optimization Of Surface Roughnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

Ayyıldız

Kara

2021

Advances in Materials Science and Engineering

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“…It was found that the surface roughness can be optimized with a higher cutting speed and lower feed rate values. Based on the analysis of variance, Ayyildiz [12] found the most effective parameter affecting the surface roughness is the depth of cut while the factor ranked second is cutting the speed. Li et al [13] investigated the effects of input parameters on specific cutting energy during the MDF helical up-milling process.…”

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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“…In the milling process, the surface roughness is one of the important properties indicating workpiece quality. Models used in the prediction of surface roughness include the multiple regression technique, fuzzy set-based technique, and artificial neural networks (ANNs) [3][4][5][6][7]. e prediction of surface roughness (Ra) values in Al alloy 7075-T7351 in the face milling machining process for different ANNs was developed by Munoz-Escalona and Maropoulo [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

Eser

Ayyıldız

et al. 2021

Advances in Materials Science and Engineering

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This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

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Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

Cited by 8 publications

References 50 publications

Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

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

Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

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