Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood

Tiryaki, Sebahattin; Özşahin, Şükrü; Aydın, Aytaç

doi:10.1007/s00107-016-1050-1

Cited by 34 publications

(21 citation statements)

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“…The performance of each model was analyzed via several indicators that are frequently used in the literature, such as correlation coefficient (R), coefficient of determination (R 2 ), root mean square error (RMSE), mean square error (MSE), and mean absolute prediction error (MAPE) (Tiryaki and Aydin 2014;Watanabe et al 2015;Fu et al 2017). Among these criteria, Tiryaki et al (2017) claim that MAPE is the most important determinant of model performance. The correlation coefficient (R) and the coefficient of determination (R 2 ) were calculated by means of Eqs.…”

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confidence: 99%

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Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Răcășan

Bedelean

Georgescu

et al. 2020

BioRes

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The bending moment capacity of heat-treated wood dowel joints loaded in compression or tension was predicted via two artificial neural network (ANN) models. Additionally, a comparative study between similar models that were developed through response surface methodology (RSM) was performed. The joints were made of heat-treated ash (Fraxinus excelsior). The values of the ultimate failure load and the moment arms were recorded for each run via a universal testing machine. To develop the ANN models, the experimental data were randomly divided into three subsets, which were needed for the training, testing, and validation phases. The RSM models were obtained from the literature. The performances of the models were analyzed in terms of the correlation coefficient, coefficient of determination, root mean square error, mean square error, and mean absolute prediction error. A sensitivity analysis was also performed to observe potential changes in the results due to the uncertainty in the input variables. The ANN model better predicted the bending moment capacity of heat-treated wood dowel joints loaded in compression than the RSM model. In contrast, the RSM model predicted the bending moment capacity of joints loaded in tension more accurately than the ANN model.

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confidence: 99%

“…The ANN and RSM modeling techniques have been joined to optimize the flexural properties of gypsum-bonded fiberboards (Nazerian et al 2018) and to determine the optimum surface roughness and lower power consumption in abrasive machining processes of wood (Tiryaki et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Răcășan

Bedelean

Georgescu

et al. 2020

BioRes

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“…It can better design experimental scheme and process experimental data. Tiryaki et al (2016) investigated using artificial neural network to derive a mathematical model that is based on the data of the various processing parameters on the surface roughness of wood in abrasive machining process; it was found that the impact of power consumption determined the optimal cutting parameter value to minimize cost. Stanojevic et al (2017) studied the influence of the feed speed, cutting depth, and front angle on surface roughness and power consumption by using the neural fuzzy method and represented it in a model.…”

Section: Introductionmentioning

confidence: 99%

The effect of bamboo surface roughness of cutting parameters on the bamboo milling

Liu

Zhou

Wan-si

et al. 2020

BioRes

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The surface roughness of bamboo is a factor that determines the processing quality. The effects of spindle speed, feed speed, and cutting depth on the surface roughness of bamboo were studied using the orthogonal design test method. The results were analyzed using range analysis, main effect analysis, interaction analysis, and variance analysis to determine the effect of cutting parameters on the surface roughness of bamboo. The feed speed had a greater effect on the surface roughness of the bamboo in the tangential section; the spindle speed had a greater impact on the surface roughness of the bamboo in the cross section. The cutting depth had a greater impact on the surface roughness of the bamboo in the radial section. For the surface roughness on the tangential, cross, and radial sections of the bamboo, there was a relationship with the cutting parameters. A mathematical model for the analysis of surface roughness was established by response surface method. Also, contour and surface plots based on regression models showed the correlation between surface roughness and all possible pairwise combinations of three cutting parameter variables. The cutting technology with the best surface quality was determined by optimization analysis.

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“…Tiryaki ve ark. [21] tarafından yapılan bir diğer çalışmada, odun türü, basınç miktarı, işleme hızı, aşındırıcı tipi ve aşındırıcı tane sayısı değişkenlerinin zımparalama işleminde yüzey pürüzlülüğü ve güç tüketim düzeyleri üzerine etkileri literatürden elde edilen veriler kullanılarak YSA yaklaşımı ile modellenmiştir. Odunun yüzey pürüzlülüğünün tahmini için bazı YSA modelleri geliştirilmiş olsa da, daha fazla araştırmaya ihtiyaç duyulduğu açıktır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

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Singer

2019

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ÖZMobilya ve dekorasyon elemanlarının üretiminde kullanılan ağaç malzemelerin yüzey pürüzlülüğünün ve adezyon direncinin belirlenmesi, nihai ürünün kalitesinin değerlendirilmesi bakımından çok önemlidir. Bu makalede ilk olarak, odun türü, kesme yönü ve zımpara kağıdı türünün yüzey pürüzlülüğü üzerine etkilerini incelemek için yapay sinir ağı (YSA) ile yüzey pürüzlülüğü tahmin modeli geliştirilmiştir. Daha sonra, vernik türü, odun türü, kesme yönü ve yüzey pürüzlülüğünün adezyon direnci üzerine etkileri YSA ile geliştirilen adezyon direnci tahmin modeliyle araştırılmıştır. En iyi performansa sahip tahmin modelleri istatistiksel ve grafiksel karşılaştırmalar yoluyla belirlenmiştir. YSA modellerinin kabul edilebilir sapmalarla oldukça tatmin edici neticeler elde ettiği görülmüştür. Sonuç olarak bu çalışmanın bulguları, deneysel araştırmalar için zaman, enerji ve maliyeti azaltmak amacıyla mobilya ve dekorasyon endüstrisinde etkili bir şekilde uygulanabilir. ABSTRACTDetermining the surface roughness and adhesion strength of wood materials used in the manufacturing of furniture and decoration elements is very crucial in terms of evaluating the quality of the final product. In this article, firstly, the surface roughness prediction model was developed with the artificial neural network (ANN) to examine the effects of wood species, cutting direction and sandpaper type on surface roughness. Then, the effects of varnish type, wood species, cutting direction and surface roughness on adhesion strength were investigated with the adhesion strength prediction model developed with ANN. The prediction models with the best performance were determined by statistical and graphical comparisons. It has been observed that ANN models yielded very satisfactory results with acceptable deviations. As a result, the findings of this study could be employed effectively into the furniture and decoration industry to reduce time, energy and cost for empirical investigations.

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Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood

Cited by 34 publications

References 34 publications

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

Comparison between artificial neural networks and response surface methodology to predict the bending moment capacity of heat-treated wood dowel joints

The effect of bamboo surface roughness of cutting parameters on the bamboo milling

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

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