Artificial neural network modeling to predict optimum power consumption in wood machining

• Autorzy: Tiryaki S. , Malkoçoğlu A. , Özşahin S.

Identyfikatory

DOI

10.12841/wood.1644-3985.140.08

• Języki publikacji: EN

Abstrakty

EN

This paper investigates and models the effects of wood species, feed rate, number of cutters and cutting depth on power consumption during the wood planing process. For this purpose, the samples were planed at a feed rate of 7 and 14 m/min, a cutting depth of 0.5, 1.5, 2.5 and 3.5 mm, and using 1, 2 and 4 cutters, with measurements taken during this process. According to the results, power consumption increased with increasing feed rate, cutting depth and number of cutters. In artificial neural network model, the mean absolute percentage error values between the actual and predicted values were 0.32% for the training data set and 1.15% for the testing data set. In addition, the values of R² were found to be 0.99 and 0.97 in the training and testing data sets, respectively. It is evident from the results that the designed model may be used to optimize the effects of process parameters on power consumption during the planing process of different wood species. Thus, the findings of the current study can be effectively applied in the wood machining industry in order to reduce the time for further experimental investigations, to lower energy consumption and avoid high machining costs.

Słowa kluczowe

EN

neural network modeling; optimization; planing; power consumption; wood machining

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Drewno : prace naukowe, doniesienia, komunikaty
• Rocznik: 2016
• Tom: vol. 59, nr 196
• Strony: 109--125
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Tiryaki S.

• Karadeniz Technical University, Faculty of Forestry, Department of Forest Industry Engineering, 61080, Trabzon, Turkey

autor

Malkoçoğlu A.

• Karadeniz Technical University, Faculty of Forestry, Department of Forest Industry Engineering, 61080, Trabzon, Turkey

autor

Özşahin S.

• Karadeniz Technical University, Faculty of Engineering, Department of Industrial Engineering, 61080 Trabzon, Turkey

Bibliografia

• Aguilera A., Martin P. [2001]: Machining qualification of solid wood of Fagus Silvatica L. and Picea Excelsa L.; cutting forces, power requirements and surface roughness. Holz als Roh- und Werkstoff 59: 483-488
• Avramidis S., Iliadis L. [2005]: Predicting wood thermal conductivity using artificial neural networks. Wood and Fiber Science 37: 682-690
• Avramidis S., Wu H. [2007]: Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. Holz als Roh- und Werkstoff 65: 89-93
• Bozkurt A.Y. [1985]: Ağaç malzemede liflere paralel yönde periferik (çevresel) kesiş (Peripheral cutting in a parallel direction to the fibres in wood material). İstanbul Üniversitesi Orman Fakültesi Dergisi 35: 17-26
• Canakci A., Ozsahin S., Varol T. [2012]: 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
• Ceylan I. [2008]: Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology 26: 1469-1476
• Cook D.F., Ragsdale C.T., Major R.L. [2000]: Combining a neural network with a genetic algorithm for process parameter optimization. Engineering Applications of Artifıcial Intelligence 13: 391-396
• Cristovao L. [2012]: Main cutting force models for two species of tropical wood. Wood Material Science and Engineering 7: 1-7
• Gunay M. [2003]: Experimental investigation of the influence of cutting tool rake angle on forces during metal cutting. Master's thesis, Gazi University, Ankara, Turkey
• Gurleyen L. [2010]: The study for the strain of hardwood materials against machines and cutters in planning process. Scientific Research and Essays 5: 3903-3913
• Gurleyen L., Budakci M. [2015]: Determination of machinery and knife strains in the planing of wood-based panels. Journal of Wood Science 61: 391-400
• Gurleyen L., Subasi S. [2009]: The compulsions which the hard tree materials show against to the cutters and machine in planing process. Journal of the Faculty of Engineering and Architecture of Gazi University 24: 209-219
• Ilhan R., Burdurlu E., Baykan I. [1990]: Ağaçişlerinde kesme teorisi ve mobilya endüstrisi makineleri (Cutting theory in woodworking and furniture industry machines). Gazi Yayınevi, Ankara.
• Joo S.B., Oh S.E, Sim T, Kim H, Choi C.H, Koo H, Mun J.H. [2014]: Prediction of gait speed from plantar pressure using artificial neural networks. Expert Systems with Applications 41: 7398-7405
• Kalogirou S.A. [2001]: Artificial neural networks in renewable energy system applications: a review. Renewable and Sustainable Energy Reviews 5: 373-401
• Khalid M., Lee E.L.Y., Yusof R., Nadaraj M. [2008]: Design of an intelligent wood species recognition system. International Journal of Simulation: Systems, Science and Technology 9: 9-19
• Khayet M., Cojocaru C. [2013]: Artificial neural network model for desalination by sweeping gas membrane distillation. Desalination 308: 102-110
• Korkut I., Donertas M.A., Seker U. [1999]: Üç boyutlu dinamometre tasarımı ve imalatı (Three dimensional dynamometer design and production). Teknoloji 2: 115-129
• Kurdthongmee W. [2008]: Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing. Computers and Electronics in Agriculture 64: 85-92
• Mendoza B.A. [1988]: The effect of density and some machining variables on power consumption and planing quality of coconut (Cocos nucifera L.) lumber. Journal of Forest Products Research and Development Institute 17: 37-66
• Okan O.T., Deniz I., Tiryaki S. [2015]: Application of artificial neural networks for predicting tensile index and brightness in bleaching pulp. Maderas-Ciencia Y Tecnologia 17 [3]: 571-584
• Ozsahin S. [2013]: Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. European Journal of Wood and Wood Products 71: 769-777
• Panda L., Tripathy S.K. [2014]: Performance prediction of gravity concentrator by using artificial neural network – a case study. International Journal of Mining Science and Technology 24: 461-465
• Qazi A., Fayaz H., Wadi A., Raj R.G., Rahim N.A., Khan W.A. [2015]: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. Journal of Cleaner Production 104: 1-12
• Ritchie M.D., White B.C., Parker J.S., Hahn L.W., Moore J.H. [2003]: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4: 28
• Scott G.M., Ray W.H. [1993]: Creating efficient nonlinear neural network process model that allow model interpretation. Journal of Process Control 3 [3]: 163-178
• Sofuoglu S.D. [2015]: Using artificial neural networks to model the surface roughness of massive wooden edge-glued panels made of Scotch pine (Pinus sylvestris L.) in a machining process with computer numerical control. Bioresources 10 [4]: 6797-6808
• Srisaeng P., Baxter G.S., Wild G. [2015]: Forecasting demand for low cost carriers in Australia using an artificial neural network approach. Aviation 19 [2]: 90-103
• Stewart H.A. [1974]: Comparison of factors affecting power for abrasive and knife planing of hardwoods. Forest Products Journal 24: 31-34
• Tiryaki S., Hamzacebi C. [2014]: Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement 49: 266-274
• Tiryaki S., Bardak S., Bardak T. [2015]: 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 [23]: 2521-2536
• Tiryaki S., Malkocoglu A., Ozsahin S. [2014]: Using artificial neural networks for modeling surface roughness of wood in machining process. Construction and Building Materials 66: 329-335
• Wu H., Avramidis S. [2006]: Prediction of timber kiln drying rates by neural networks. Drying Technology 24: 1541-1545
• Yang H., Cheng W., Han G. [2015]: Wood modification at high temperature and pressurized steam: a relational model of mechanical properties based on a neural network. Bioresources 10 [3]: 5758-5776
• Yuce B., Mastrocinque E., Packianather M.S., Pham D., Lambiase A., Fruggiero F. [2014]: Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects. Production & Manufacturing Research 2 [1]: 291-308
• Zhang G., Ptuwo B.E., Hu M.Y. [1998]: Forecasting with ANN: the state of the art. International Journal of Forecasting 14: 35-62
• Zhang J., Cao J., Zhang D. [2006]: ANN-based data fusion for lumber moisture content sensors. Transactions of the Institute of Measurement and Control 28: 69-79

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.