Optimization of CNC operating parameters to minimize surface roughness of Pinus sylvestris using integrated artificial neural network and genetic algorithm

Gürgen, Ayşenur; Çakmak, Ali; Yıldız, Sibel; Malkoçoğlu, Abdülkadir

doi:10.4067/s0718-221x2022000100401

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…While Hazir and Ozcan (2019) found these values as 8 mm cutting tool diameter, 17377 rpm spindle speed, and 2.012 m/min feed rate for cedar wood, Hazir and Koc (2019) in another study determined as 9.88 mm cutting tool diameter, 21000 rpm spindle speed, and 2.21 m/min feed rate for beech wood. Furthermore, Gürgen et al (2022) found the optimum parameter results for pine wood as 17900 rpm spindle speed and 3 m/min feed rate. In this study, the samples with the highest wettability were obtained in walnut wood with a 4 mm cutting tool diameter, 14000 rpm spindle speed and 3 m/min feed rate while these values were determined as 3 mm cutting tool diameter, 12000 rpm spindle speed and 9 m/min feed rate in the ash wood.…”

Section: Optimization Resultsmentioning

confidence: 99%

Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

Cakiroglu

Demir

Aydın

et al. 2022

BioRes

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This study aimed to predict the CNC cutting conditions for the best wood surface quality, energy, and time savings using artificial neural network (ANN) models. In the CNC process, walnut, and ash wood were used as materials, while three different cutting tool diameters (3 mm, 6 mm, and 8 mm), spindle speed (12000 rpm, 15000 rpm, and 18000 rpm), and feed rate (3 m/min, 6 m/min, and 9 m/min) were determined as cutting conditions. After the cutting processes were completed with the CNC machine, energy consumption and processing time were determined for all groups. Surface roughness and wettability tests were performed on the processed wood samples, and their surface qualities were determined. The experimentally obtained data were analysed in ANN, and the models with the best performance were obtained. By using these prediction models, optimum cutting conditions were determined. Using the findings of the study, the optimum cutting condition values can be determined for walnut and ash wood with the smoothest and best wettable surface. Furthermore, in CNC processes using such materials, minimum energy consumption and shorter processing time can be obtained with optimum cutting conditions.

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Section: Optimization Resultsmentioning

confidence: 99%

Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

Cakiroglu

Demir

Aydın

et al. 2022

BioRes

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“…They are suitable for modeling various manufacturing functions due to their ability to learn complex non-linear and multivariable relationships between process parameters (Karayel, 2009). Using artificial neural networks (ANNs) have been applied in wood and wood-based materials science and the wood machining industry, such as in recognition of wood species (Esteban et al, 2009), the drying of solid wood (Wu and Avramidis, 2007) the mechanical properties (Fernández et al, 2012;Tiryaki and Aydin, 2014), machining parameters optimization (Sofuoglu, 2015;Gurgen et al, 2021), wood surface roughness (Ayanleye et al, 2021;Gurgen et al, 2021) the classification of wood and wood-based materials defects (Avramidis and Iliadis, 2005;Pan et al, 2021), the analysis of moisture (Zhang et al, 2016), noise emission in the machining of wood (Ozşahin and Singer, 2022) and fracture toughness of wood (Samarasinghe and Jamieson, 2007).…”

Section: Introductionmentioning

confidence: 99%

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Bilge International Journal of Science and Technology Research

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In this study, an approach for artificial neural network (ANN) was presented to predict and control arithmetical mean surface roughness value (Ra), machining properties of wood materials densified by compressing in a computer numerical control (CNC) machine. Black poplar (Populus nigra L.) tree species were used as the experimental material. After specimens were densified by Thermo-Mechanical (TM) method at 0%, 20%, and 40% ratios, machining process of specimens were performed at 1000, 1500, and 2000 mm/min feed speeds and in 12000, 15000, 18000 rpm rotation speed on a CNC vertical wood machining center by using two different cutters. Data used for the training and testing of an ANN. Cutter type, compression ratio, feed rate, and spindle speed were selected as Four parameters. While hidden layer of the Ra model has ten neurons, one hidden layer was used, Compression ratio is the most significant parameter, followed by feed speed for Ra values. surface roughness increases with increased feed rate. Ra values in training, validation, and testing the data set for Ra were 0.97122, 0.8538, and 0.76685, respectively. The Mean Square Error (MSE) value was determined as 0.0019914 test of the network. The proposed ANN model came to agreement with the measured values in predicting surface roughness Ra values of MAPE. The MAPE value was calculated as 6.61, which can be considered a very good prediction (MAPE< 10 % = very good prediction). The study showed that obtained ANN prediction model is a practical and efficient tool to model the Ra of wood. For reducing energy, time and cost in the wood industry (densification and CNC wood machining), current research results can be implemented.

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“…Usually, when measuring the texture of a machined surface, it is the roughness that is analyzed, while waviness and shape deviation are the elements that are filtered out of the collected data. Roughness can occur as a result of processing conditions and regimes, or as a result of the internal structure of the wood's anatomical structure [1].…”

Section: Introductionmentioning

confidence: 99%

Mathematical model of the influence of some parameters of solid wood machinability on the roughness of the processed surface Ra during planning

Hodžić,

Bijelić,

Hodžić

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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We consider the machinability of the material as a technological feature that expresses the ability of the material to remove the maximum number of shavings from its machined surface in the minimum time with satisfactory processing quality, with as little cutting force as possible and as little tool wear as possible. The aim of the experimental research in this work is to examine the significance of the influential kinematic parameters of the roughness of the machined surface, i.e. of wood density (ρ), feeding speed (s’) and the number of spiral cutting knices (z) in the process of planning massive wood on the roughness of the newly created processing surface, which will vary in 14 trials, of which 6 are repetitions in the central point of the compositional plan, where the roughness parameter Ra is obtained as an output value, and the analysis of experimental data from the point of view of possible achievement of a better quality of the processed surface. The obtained mathematical model is essentially applicable and can be used to optimize the machinability parameters in the planning process of solid wood, and the experimental results can be used in further research into other parameters of the machinability of solid wood in the planning process.

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Optimization of CNC operating parameters to minimize surface roughness of Pinus sylvestris using integrated artificial neural network and genetic algorithm

Cited by 9 publications

References 9 publications

Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

Prediction of optimum CNC cutting conditions using artificial neural network models for the best wood surface quality, low energy consumption, and time savings

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

Mathematical model of the influence of some parameters of solid wood machinability on the roughness of the processed surface Ra during planning

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