Abstract
One of the biggest challenges in machining processes of wood is to detect the optimum values of process parameters for reducing the final production cost. In the present study, the effects of various process parameters on surface roughness and power consumption in abrasive machining process of wood using experimental data collected from the literature were modeled by artificial neural networks (ANNs). The results have indicated that accurate prediction of the experimental data by neural network models was achieved with the mean absolute percentage error (MAPE) less than 2.51 % for power consumption and 2.65 % for surface roughness in the testing phase. Besides, the values of determination coefficient (R2) were found as 0.994 and 0.985 in the prediction of surface roughness and power consumption by the ANN modeling, respectively. Based on the results, it can be said that by means of the proposed models the surface roughness and power consumption can easily be predicted with very high degrees of accuracy in abrasive machining process of wood. Consequently, the present study can effectively be applied to the wood industry to reduce the time, energy consumption and high experimental costs because it eliminates the need for a large number of experiments.
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References
Avramidis S, Iliadis L (2005) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci 37:682–690
Avramidis S, Wu H (2007) Artificial neural network and mathematical modeling comparative analysis of no isothermal diffusion of moisture in wood. Holz Roh-Werkst 65:89–93
Aydin I (2004) Activation of wood surfaces for glue bonds by mechanical pre-treatment and its effects on some properties of veneer surfaces and plywood panels. Appl Surf Sci 233:268–274
Bajic D, Lela B, Zivkovic D (2008) Modeling of machined surface roughness and optimization of cutting parameters in face milling. Metalurgija 47(4):331–334
Buratti C, Barelli L, Moretti E (2013) Wooden windows: sound insulation evaluation by means of artificial neural networks. Appl Acoust 74:740–745
Burdurlu E, Usta I, Ulupinar M, Aksu B, Erarslan TC (2005) 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. Turk J Agric For 29:315–321
Castellani M, Rowlands H (2008) Evolutionary feature selection applied to artificial neural networks for wood veneer classification. Int J Prod Res 46:3085–3105
Ceylan I (2008) Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technol 26:1469–1476
Cool J, Hernandez RE (2011) Improving the sanding process of black spruce wood for surface quality and water-based coating adhesion. Forest Prod J 61(5):372–380
Cristovao L, Ekevad M, Gronlund A (2013) Industrial sawing of Pinus sylvestris L.: power consumption. Bioresources 8(4):6044–6053
Cus F, Zuperl U, Gecevska V (2007) High speed end-milling optimization using particle swarm intelligence. J Achievements Mater Manuf Eng 22(2):75–78
Custodio J, Broughton J, Cruz H (2009) A review of factors influencing the durability of structural bonded timber joints. Int J Adhes Adhes 29:173–185
Davim JP, Gaitonde VN, Karnik SR (2008) Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J Mater Process Technol 205:16–23
De Moura LF, Hernandez RE (2006) Effects of abrasive mineral, grit size and feed speed on the quality of sanded surfaces of sugar maple wood. Wood Sci Technol 40:517–530
Esteban LG, Fernandez FG, de Palacios P (2009) MOE prediction in Abies pinsapo boiss. timber: application of an artificial neural network using non-destructive testing. Comput Struct 87:1360–1365
Fausett L (1994) Fundamentals of neural network architecture, algorithms, and applications. Prentice-Hall, New Jersey
Fotin A, Cismaru I, Salca EA (2008) Experimental research concerning the power consumption during the sanding process of birch wood. Pro Ligno 4(3):37–45
Gago J, Martinez-Nunez L, Landin M, Gallego PP (2010) Artificial neural networks as an alternative to the traditional statistical methodology in plant research. J Plant Physiol 167:23–27
Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67
Hemmat Esfe M, Afrand M, Yan WM, Akbari M (2015) Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data. Int Commun Heat Mass Transfer 66:246–249
Javorek L, Hric J, Vacek V (2006) The study of chosen parameters during sanding of spruce and beech wood. Pro Ligno 2(4):1–11
Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustainable Energy Rev 5:373–401
Kant G, Sangwan KS (2015) Predictive modeling for power consumption in machining using artificial intelligence techniques. Procedia CIRP 26:403–407
Khalid M, Lee ELY, Yusof R, Nadaraj M (2008) Design of an intelligent wood species recognition system. Int J Simul Syst Sci Technol 9:9–19
Koch P (1964) Wood machining processes. Ronald Press, New York
Leahy P, Kiely G, Corcoran G (2008) Structural optimisation and input selection of an artificial neural network for river level prediction. J Hydrol 355:192–201
Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205:439–450
Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Mining Sci 36:29–39
Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater Des 27:735–744
Ozsahin S (2013) Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. Eur J Wood Prod 71:769–777
Ozsahin S, Aydin I (2014) Prediction of the optimum veneer drying temperature for good bonding in plywood manufacturing by means of artificial neural network. Wood Sci Technol 48:59–70
Phate MR, Tatwawadi VH (2013) Modeling of power consumption in turning of ferrous and nonferrous materials using artificial neural network. Int J Eng Trend Tech 4(3):236–241
Ratnasingam J, Reid HF, Perkins MC (2002) The abrasive sanding of rubberwood (Hevea brasiliensis): an industrial perspective. Holz Roh Werkst 60:191–196
Richter K, Feist WC, Knaebe MT (1995) The effect of surface roughness on the performance of finishes Part 1. roughness characterization and stain performance. forest. Prod J 45(7/8):91–97
Saloni DE, Lemaster RL, Jackson SD (2005) Abrasive machining process characterization on material removal rate, final surface texture, and power consumption for wood. Forest Prod J 55(12):35–41
Saloni DE, Lemaster RL, Jackson SD (2010) Process monitoring evaluation and implementation for the wood abrasive machining process. Sensors 10:10401–10412
Sandak J (2011) Modeling wood surface geometry after wood machining. In: Proceedings for 20th IWMS June 7–10, Skelleftea, Sweden, pp 173–180
Sinn G, Gindl M, Reiterer A, Stanzl-Tschegg S (2004) Changes in the surface properties of wood due to sanding. Holzforschung 58(3):246–251
Sulaiman O, Hashim R, Subari K, Liang CK (2009) Effect of sanding on surface roughness of rubberwood. J Mater Process Technol 209:3949–3955
Taylor JB, Carrano AL, Lemaster RL (1999) Quantification of process parameters in a wood sanding operation. Forest Prod J 49(5):41–46
Tiryaki S, Aydin A (2014) An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Const Build Mater 2014(62):102–108
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, Malkocoglu A, Ozsahin S (2014) Using artificial neural networks for modeling surface roughness of wood in machining process. Const Build Mater 66:329–335
Tiryaki S, Hamzacebi C, Malkocoglu A (2015) Evaluation of process parameters for lower surface roughness in wood machining by using Taguchi design methodology. Eur J Wood Prod 73:537–545
Varol T, Canakci A, Ozsahin S (2013) Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024–B4C composites produced by powder metallurgy. Compos B 54:224–233
Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst Appl 37:1755–1768
Zhang G, Ptuwo BE, Hu MY (1998) Forecasting with ANN: the state of the art. Int J Forecasting 14:35–62
Zielinska S, Kepczynska E (2013) Neural modeling of plant tissue cultures: a review. BioTechnologia 94(3):253–268
Acknowledgments
The authors would like to thank Daniel E. Saloni, Richard L. Lemaster, and Steven D. Jackson from North Carolina State University, USA, for providing the database used in the paper.
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Tiryaki, S., Özşahin, Ş. & Aydın, A. Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood. Eur. J. Wood Prod. 75, 347–358 (2017). https://doi.org/10.1007/s00107-016-1050-1
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DOI: https://doi.org/10.1007/s00107-016-1050-1