Araştırma Makalesi
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Prediction of Surface Roughness and Adhesion Strength of Wood by Artificial Neural Networks

Yıl 2019, Cilt: 22 Sayı: 4, 889 - 900, 01.12.2019
https://doi.org/10.2339/politeknik.481762

Öz

Determining 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.

Kaynakça

  • Priadi T. and Hiziroglu S., ''Characterization of heat treated wood species'', Materials and Design, 49: 575-582, (2013).
  • Akbiyik A., Lamanna A. J. and Hale W. M., ''Feasibility investigation of the shear repair of timber stringers with horizontal splits'', Construction and Building Materials, 21: 991-1000, (2007).
  • Hauptmann M., Müller U., Obersriebnig M., Gindl-Altmutter W., Beck A. and Hansmann C., ''The optical appearance of wood related to nanoscale surface roughness'', BioResources, 8: 4038-4045, (2013).
  • Kılıç M., ''The effects of steaming of beech (Fagus orientalis L.) and sapele (Entandrophragma cylindricum) woods on the adhesion strength of varnish'', Journal of Applied Polymer Science, 113: 3492-3497, (2009).
  • Vitosytė J., Ukvalbergienė K. and Keturakis G., ''The effects of surface roughness on adhesion strength of coated ash (Fraxinus excelsior L.) and birch (Betula L.) wood'', Materials Science, 18: 347-351, (2012).
  • Ratnasingam J. and Scholz F., ''Optimal surface roughness for high-quality finish on rubberwood (Hevea brasiliensis)'', Holz als Roh- und Werkstoff, 64: 343-345, (2006).
  • Magoss E., ''General regularities of wood surface roughness'', Acta Silvatica & Lignaria Hungarica, 4: 81-93, (2008).
  • Sofuoğlu S. D. and Kurtoğlu A., ''Effects of machining conditions on surface roughness in planing and sanding of solid wood'', Drvna Industrija, 66: 265-272, (2015).
  • Burdurlu E., Usta İ., Ulupınar M., Aksu B. and Erarslan T. Ç., ''The effect of the number of blades and the grain size of abrasives in planing and sanding on the surface roughness of European black pine and Lombardy poplar'', Turkish Journal of Agriculture and Forestry, 29: 315-321, (2005).
  • Singer H. and Özşahin Ş., ''Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process'', Turkish Journal of Agriculture and Forestry, 42: 364-371, (2018).
  • Hendarto B., Shayan E., Ozarska B. and Carr R., ''Analysis of roughness of a sanded wood surface'', International Journal of Advanced Manufacturing Technology, 28: 775-780, (2006).
  • Haghbakhsh R., Adib H., Keshavarz P., Koolivand M. and Keshtkari S., ''Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions'', Thermochimica Acta, 551: 124-130, (2013).
  • Ozsahin S. and Murat M., ''Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks'', European Journal of Wood and Wood Products, 76: 563-572, (2018).
  • Avramidis S. and Iliadis L., ''Predicting wood thermal conductivity using artificial neural networks'', Wood and Fiber Science, 37: 682-690, (2005).
  • Samarasinghe S., Kularisi D. and Jamieson T., ''Neural networks for predicting fracture toughness of individual wood samples'', Silva Fennica, 41: 105-122, (2007).
  • Castellani M. and Rowlands H., ''Evolutionary feature selection applied to artificial neural networks for wood-veneer classification'', International Journal of Production Research, 46: 3085-3105, (2008).
  • Ceylan İ., ''Determination of drying characteristics of timber by using artificial neural networks and mathematical models'', Drying Technology, 26: 1469-1476, (2008).
  • Özşahin Ş., ''The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board'', BioResources, 7: 1053-1067, (2012).
  • Ozsahin S., ''Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis'', European Journal of Wood and Wood Products, 71: 769-777, (2013).
  • Tiryaki S., Malkoçoğlu A. and Özşahin Ş., ''Using artificial neural networks for modeling surface roughness of wood in machining process'', Construction and Building Materials, 66: 329-335, (2014).
  • Tiryaki S., Özşahin Ş. and Aydın A., ''Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood'', European Journal of Wood and Wood Products, 75: 347-358, (2017).
  • Tiryaki S., Özşahin Ş. and Yıldırım İ., ''Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods'', International Journal of Adhesion and Adhesives, 55: 29-36, (2014).
  • Tiryaki S., Bardak S. and Bardak T., ''Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive'', Journal of Adhesion Science and Technology, 29: 2521-2536, (2015).
  • Khanlou H. M., Sadollah A., Ang B. C., Kim J. H., Talebian S. and Ghadimi A., ''Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks'', Neural Computing & Applications, 25: 767-777, (2014).
  • Abbot J. and Marohasy J., ''Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks'', Atmospheric Research, 138: 166-178, (2014).
  • Yadav A. K. and Chandel S. S., ''Solar radiation prediction using Artificial Neural Network techniques: A review'', Renewable & Sustainable Energy Reviews, 33: 772-781, (2014).
  • Fathi M., Mohebbi M. and Razavi S. M. A., ''Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit'', Food and Bioprocess Technology, 4: 1357-1366, (2011).
  • Canakci A., Ozsahin S. and Varol T., ''Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks'', Powder Technology, 228: 26-35, (2012).
  • Ariana M. A., Vaferi B. and Karimi G., ''Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks'', Powder Technology, 278: 1-10, (2015).
  • Hamzehie M. E., Fattahi M., Najibi H., Van der Bruggen B. and Mazinani S., ''Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2) in 32 commonly ionic liquid and amine solutions'', Journal of Natural Gas Science and Engineering, 24: 106-114, (2015).
  • Monjezi M., Hasanipanah M. and Khandelwal M., ''Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network'', Neural Computing & Applications, 22: 1637-1643, (2013).
  • Roy S., Banerjee R. and Bose P. K., ''Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network'', Applied Energy, 119: 330-340, (2014).
  • Yildirim I., Ozsahin S. and Akyuz K. C., ''Prediction of the financial return of the paper sector with artificial neural networks'', BioResources, 6: 4076-4091, (2011).
  • Söğütlü C., Nzokou P., Koc I., Tutgun R. and Döngel N., ''The effects of surface roughness on varnish adhesion strength of wood materials'', Journal of Coatings Technology and Research, 13: 863-870, (2016).
  • TS 2470, ''Wood–Sampling methods and general requirements for physical and mechanical tests'', (1976).
  • TS 2471, ''Wood, determination of moisture content for physical and mechanical tests'', (2005).
  • ASTM-D 1667-87, ''Standard methods for conducting machining tests of wood and wood-base materials'', (1999).
  • ISO 4287, ''Geometrical product specifications surface texture profile method terms, definitions and surface texture parameters'', (1997).
  • TS 2495 EN ISO 3274, ''Geometrical product specifications (GPS)–Surface texture: Profile method–Nominal characteristics of contact (stylus) instruments'', (2005).
  • ASTM-D 3023, ''Standard practice for determination of resistance of factory applied coatings on wood products to stains and reagents'', (1998).
  • TS EN ISO 4624, ''Paints and varnishes–pull-off test for adhesion'', (2006).
  • Seyhan M., Akansu Y. E., Murat M., Korkmaz Y. and Akansu S. O., ''Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network'', International Journal of Hydrogen Energy, 42: 25619-25629, (2017).
  • Küçükönder H., Boyacı S. and Akyüz A., ''A modeling study with an artificial neural network: developing estimation models for the tomato plant leaf area'', Turkish Journal of Agriculture and Forestry, 40: 203-212, (2016).
  • Varol T., Canakci A. and Ozsahin S., ''Prediction of effect of reinforcement content, flake size and flake time on the density and hardness of flake AA2024-SiC nanocomposites using neural networks'', Journal of Alloys and Compounds, 739: 1005-1014, (2018).
  • Budakçı M. and Sönmez A., ''Determining adhesion strength of some wood varnishes on different wood surfaces'', Journal of the Faculty of Engineering and Architecture of Gazi University, 25: 111-118, (2010).

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

Yıl 2019, Cilt: 22 Sayı: 4, 889 - 900, 01.12.2019
https://doi.org/10.2339/politeknik.481762

Öz

Mobilya 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.  

Kaynakça

  • Priadi T. and Hiziroglu S., ''Characterization of heat treated wood species'', Materials and Design, 49: 575-582, (2013).
  • Akbiyik A., Lamanna A. J. and Hale W. M., ''Feasibility investigation of the shear repair of timber stringers with horizontal splits'', Construction and Building Materials, 21: 991-1000, (2007).
  • Hauptmann M., Müller U., Obersriebnig M., Gindl-Altmutter W., Beck A. and Hansmann C., ''The optical appearance of wood related to nanoscale surface roughness'', BioResources, 8: 4038-4045, (2013).
  • Kılıç M., ''The effects of steaming of beech (Fagus orientalis L.) and sapele (Entandrophragma cylindricum) woods on the adhesion strength of varnish'', Journal of Applied Polymer Science, 113: 3492-3497, (2009).
  • Vitosytė J., Ukvalbergienė K. and Keturakis G., ''The effects of surface roughness on adhesion strength of coated ash (Fraxinus excelsior L.) and birch (Betula L.) wood'', Materials Science, 18: 347-351, (2012).
  • Ratnasingam J. and Scholz F., ''Optimal surface roughness for high-quality finish on rubberwood (Hevea brasiliensis)'', Holz als Roh- und Werkstoff, 64: 343-345, (2006).
  • Magoss E., ''General regularities of wood surface roughness'', Acta Silvatica & Lignaria Hungarica, 4: 81-93, (2008).
  • Sofuoğlu S. D. and Kurtoğlu A., ''Effects of machining conditions on surface roughness in planing and sanding of solid wood'', Drvna Industrija, 66: 265-272, (2015).
  • Burdurlu E., Usta İ., Ulupınar M., Aksu B. and Erarslan T. Ç., ''The effect of the number of blades and the grain size of abrasives in planing and sanding on the surface roughness of European black pine and Lombardy poplar'', Turkish Journal of Agriculture and Forestry, 29: 315-321, (2005).
  • Singer H. and Özşahin Ş., ''Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process'', Turkish Journal of Agriculture and Forestry, 42: 364-371, (2018).
  • Hendarto B., Shayan E., Ozarska B. and Carr R., ''Analysis of roughness of a sanded wood surface'', International Journal of Advanced Manufacturing Technology, 28: 775-780, (2006).
  • Haghbakhsh R., Adib H., Keshavarz P., Koolivand M. and Keshtkari S., ''Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions'', Thermochimica Acta, 551: 124-130, (2013).
  • Ozsahin S. and Murat M., ''Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks'', European Journal of Wood and Wood Products, 76: 563-572, (2018).
  • Avramidis S. and Iliadis L., ''Predicting wood thermal conductivity using artificial neural networks'', Wood and Fiber Science, 37: 682-690, (2005).
  • Samarasinghe S., Kularisi D. and Jamieson T., ''Neural networks for predicting fracture toughness of individual wood samples'', Silva Fennica, 41: 105-122, (2007).
  • Castellani M. and Rowlands H., ''Evolutionary feature selection applied to artificial neural networks for wood-veneer classification'', International Journal of Production Research, 46: 3085-3105, (2008).
  • Ceylan İ., ''Determination of drying characteristics of timber by using artificial neural networks and mathematical models'', Drying Technology, 26: 1469-1476, (2008).
  • Özşahin Ş., ''The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board'', BioResources, 7: 1053-1067, (2012).
  • Ozsahin S., ''Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis'', European Journal of Wood and Wood Products, 71: 769-777, (2013).
  • Tiryaki S., Malkoçoğlu A. and Özşahin Ş., ''Using artificial neural networks for modeling surface roughness of wood in machining process'', Construction and Building Materials, 66: 329-335, (2014).
  • Tiryaki S., Özşahin Ş. and Aydın A., ''Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood'', European Journal of Wood and Wood Products, 75: 347-358, (2017).
  • Tiryaki S., Özşahin Ş. and Yıldırım İ., ''Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods'', International Journal of Adhesion and Adhesives, 55: 29-36, (2014).
  • Tiryaki S., Bardak S. and Bardak T., ''Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive'', Journal of Adhesion Science and Technology, 29: 2521-2536, (2015).
  • Khanlou H. M., Sadollah A., Ang B. C., Kim J. H., Talebian S. and Ghadimi A., ''Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks'', Neural Computing & Applications, 25: 767-777, (2014).
  • Abbot J. and Marohasy J., ''Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks'', Atmospheric Research, 138: 166-178, (2014).
  • Yadav A. K. and Chandel S. S., ''Solar radiation prediction using Artificial Neural Network techniques: A review'', Renewable & Sustainable Energy Reviews, 33: 772-781, (2014).
  • Fathi M., Mohebbi M. and Razavi S. M. A., ''Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit'', Food and Bioprocess Technology, 4: 1357-1366, (2011).
  • Canakci A., Ozsahin S. and Varol T., ''Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks'', Powder Technology, 228: 26-35, (2012).
  • Ariana M. A., Vaferi B. and Karimi G., ''Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks'', Powder Technology, 278: 1-10, (2015).
  • Hamzehie M. E., Fattahi M., Najibi H., Van der Bruggen B. and Mazinani S., ''Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2) in 32 commonly ionic liquid and amine solutions'', Journal of Natural Gas Science and Engineering, 24: 106-114, (2015).
  • Monjezi M., Hasanipanah M. and Khandelwal M., ''Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network'', Neural Computing & Applications, 22: 1637-1643, (2013).
  • Roy S., Banerjee R. and Bose P. K., ''Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network'', Applied Energy, 119: 330-340, (2014).
  • Yildirim I., Ozsahin S. and Akyuz K. C., ''Prediction of the financial return of the paper sector with artificial neural networks'', BioResources, 6: 4076-4091, (2011).
  • Söğütlü C., Nzokou P., Koc I., Tutgun R. and Döngel N., ''The effects of surface roughness on varnish adhesion strength of wood materials'', Journal of Coatings Technology and Research, 13: 863-870, (2016).
  • TS 2470, ''Wood–Sampling methods and general requirements for physical and mechanical tests'', (1976).
  • TS 2471, ''Wood, determination of moisture content for physical and mechanical tests'', (2005).
  • ASTM-D 1667-87, ''Standard methods for conducting machining tests of wood and wood-base materials'', (1999).
  • ISO 4287, ''Geometrical product specifications surface texture profile method terms, definitions and surface texture parameters'', (1997).
  • TS 2495 EN ISO 3274, ''Geometrical product specifications (GPS)–Surface texture: Profile method–Nominal characteristics of contact (stylus) instruments'', (2005).
  • ASTM-D 3023, ''Standard practice for determination of resistance of factory applied coatings on wood products to stains and reagents'', (1998).
  • TS EN ISO 4624, ''Paints and varnishes–pull-off test for adhesion'', (2006).
  • Seyhan M., Akansu Y. E., Murat M., Korkmaz Y. and Akansu S. O., ''Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network'', International Journal of Hydrogen Energy, 42: 25619-25629, (2017).
  • Küçükönder H., Boyacı S. and Akyüz A., ''A modeling study with an artificial neural network: developing estimation models for the tomato plant leaf area'', Turkish Journal of Agriculture and Forestry, 40: 203-212, (2016).
  • Varol T., Canakci A. and Ozsahin S., ''Prediction of effect of reinforcement content, flake size and flake time on the density and hardness of flake AA2024-SiC nanocomposites using neural networks'', Journal of Alloys and Compounds, 739: 1005-1014, (2018).
  • Budakçı M. and Sönmez A., ''Determining adhesion strength of some wood varnishes on different wood surfaces'', Journal of the Faculty of Engineering and Architecture of Gazi University, 25: 111-118, (2010).
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Şükrü Özşahin 0000-0001-8216-0048

Hilal Singer 0000-0003-0884-2555

Yayımlanma Tarihi 1 Aralık 2019
Gönderilme Tarihi 12 Kasım 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 22 Sayı: 4

Kaynak Göster

APA Özşahin, Ş., & Singer, H. (2019). Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini. Politeknik Dergisi, 22(4), 889-900. https://doi.org/10.2339/politeknik.481762
AMA Özşahin Ş, Singer H. Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini. Politeknik Dergisi. Aralık 2019;22(4):889-900. doi:10.2339/politeknik.481762
Chicago Özşahin, Şükrü, ve Hilal Singer. “Odunun Yüzey Pürüzlülüğünün Ve Adezyon Direncinin Yapay Sinir Ağları Ile Tahmini”. Politeknik Dergisi 22, sy. 4 (Aralık 2019): 889-900. https://doi.org/10.2339/politeknik.481762.
EndNote Özşahin Ş, Singer H (01 Aralık 2019) Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini. Politeknik Dergisi 22 4 889–900.
IEEE Ş. Özşahin ve H. Singer, “Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini”, Politeknik Dergisi, c. 22, sy. 4, ss. 889–900, 2019, doi: 10.2339/politeknik.481762.
ISNAD Özşahin, Şükrü - Singer, Hilal. “Odunun Yüzey Pürüzlülüğünün Ve Adezyon Direncinin Yapay Sinir Ağları Ile Tahmini”. Politeknik Dergisi 22/4 (Aralık 2019), 889-900. https://doi.org/10.2339/politeknik.481762.
JAMA Özşahin Ş, Singer H. Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini. Politeknik Dergisi. 2019;22:889–900.
MLA Özşahin, Şükrü ve Hilal Singer. “Odunun Yüzey Pürüzlülüğünün Ve Adezyon Direncinin Yapay Sinir Ağları Ile Tahmini”. Politeknik Dergisi, c. 22, sy. 4, 2019, ss. 889-00, doi:10.2339/politeknik.481762.
Vancouver Özşahin Ş, Singer H. Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini. Politeknik Dergisi. 2019;22(4):889-900.
 
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