Optimization of wood machining parameters in cnc routers: Taguchi orthogonal array based simulated angling algorithm
Keywords:
Cedrus libani, response surface method, softwood, surface roughness, wood materialAbstract
In the present study, two mathematical models were developed to optimize the surface roughness for machining condition of Cedar of Lebanon pine (Cedrus libani). Taguchi approach was applied to examine the effect of CNC processing variables. Quality characteristics parameters were selected as arithmetic average roughness (Ra) and average maximum height of the profile (Rz) for wood material. Analysis of variance (ANOVA) was used to determine effective machining parameters. Developed mathematical models using response surface methodology (RSM) were optimized by a combined approach of the Taguchi’s L27 orthogonal array based simulated angling algorithm (SA). Optimum machining levels for determining the minimum surface roughness values were carried out three stages. Firstly, the desirability function wasused to optimize the mathematical models. Secondly, the results obtained from the desirability function were selected as the initial point for the simulated angling algorithm. Finally, the optimum parameter values were obtained by using simulated angling algorithm. Minimum Ra value was obtained spindle speed of 17377 rpm, feed rate of 2.012 m/min, tool radius of 8 mm and depth of cut of 2.009 mm by using desirability function based simulated angling algorithm. For Rz these results were found as 16980 rpm, 2.004 m/min, 8.001mm and 2.003 mm. The R-square values of the Ra and Rz were 95.91 % and 96.12 %, respectively. The proposed models obtained the minimum surface roughness values and provided better results than the observed values.
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References
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Asiltürk, I.; Neseli, S.; Ince, A.M. 2016. Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement 78:120-128.
Asiltürk, I.; Neseli, S.; Ince, A.M. 2016. Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement 78:120-128.
Azhiri, R.B; Teimouri, R.; Baboly, M.G.; Leseman, Z. 2014. Application of Taguchi, ANFIS and grey relation analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Technol 71:279-295.
Azhiri, R.B; Teimouri, R.; Baboly, M.G.; Leseman, Z. 2014. Application of Taguchi, ANFIS and grey relation analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Technol 71:279-295.
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Bharathi, S.R; Baskar, N. 2011. Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. The International Journal of Advanced Manufacturing Technology 54:445-463.
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Deepanraj, B.; Sivasubramanian, V.; Jayaraj, S. 2017. Multi-response optimization of process parameters in biogas production from food waste using Taguchi-Grey relational analysis. Energy Conversion and Management 14:429-438.
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Hiziroglu, S. 1996. Surface roughness analysis of wood composites: A stylus method. Forest Product Journal 46:34-42.
Hiziroglu, S; Kosonkorn, P. 2006. Evaluation of surface roughness of Thai medium density fiberboard (MDF). Building and Environment 41:527-533.
Hiziroglu, S; Kosonkorn, P. 2006. Evaluation of surface roughness of Thai medium density fiberboard (MDF). Building and Environment 41:527-533.
ISO 4287. 1997. Geometrical product specifications(GPS) surface texture: profile method-terms, definitions, and surface texture profile method terms, definitions and surface texture parameters, International Organization for Standardization, Geneva
ISO 4287. 1997. Geometrical product specifications(GPS) surface texture: profile method-terms, definitions, and surface texture profile method terms, definitions and surface texture parameters, International Organization for Standardization, Geneva
Kant, G.; Sangwan, K.S. 2014. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production 83:151-164.
Kant, G.; Sangwan, K.S. 2014. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production 83:151-164.
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Koç, K.H.; Erdinler, E.S.; Hazir, E.; Öztürk, E. 2017. Effect of CNC application parameters on wooden surface quality. Measurement 107:12-18.
Magoss, E. 2008.General regularities of wood surface roughness. Acta Silv. Lign. Hung.4:81-93.
Magoss, E. 2008.General regularities of wood surface roughness. Acta Silv. Lign. Hung.4:81-93.
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Mahes, G.; Muthu, S.; Devadasan, S.R. 2015. Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77:369-381.
Majumder, H.; Paul, T.R; Dey, V.; Dutta, P.; Saha, A. 2017. Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19-30.
Majumder, H.; Paul, T.R; Dey, V.; Dutta, P.; Saha, A. 2017. Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19-30.
Ozdemir, T; Hiziroglu, S. 2007. Evaluation of some sanding factors on the surface roughness of particleboard. Silva Fennica 41:373-378.
Ozdemir, T; Hiziroglu, S. 2007. Evaluation of some sanding factors on the surface roughness of particleboard. Silva Fennica 41:373-378.
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Philbin, P; Gordon, S. 2006. Recent research on the machining of wood-based composite materials. International Journal of Machining and Machinability of Materials 1:186–201.
Prakash, S.; Palanikumar, K. 2010. Modeling for prediction of surface roughness in drilling MDF panels using response surface methodology. J Compos Mater 45:1639-1646.
Prakash, S.; Palanikumar, K. 2010. Modeling for prediction of surface roughness in drilling MDF panels using response surface methodology. J Compos Mater 45:1639-1646.
Rao, R.V.; Kalyankar, V.D. 2013. Parameter optimization of modern machining process using teaching-learning-based optimization algorithm. Engineering Application of Artificial Intelligence 26:524-531.
Rao, R.V.; Kalyankar, V.D. 2013. Parameter optimization of modern machining process using teaching-learning-based optimization algorithm. Engineering Application of Artificial Intelligence 26:524-531.
Rao, V.K.; Murthy, N.S.G.B.P. 2016. Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf .10.1007/s10845-016-1197-y.
Rao, V.K.; Murthy, N.S.G.B.P. 2016. Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf .10.1007/s10845-016-1197-y.
Ratnasingman, J; Scholz, F. 2006. Optimal surface roughness for high-quality on Rubberwood. Holzals Roh-und Werkstoff 64: 343-345.
Ratnasingman, J; Scholz, F. 2006. Optimal surface roughness for high-quality on Rubberwood. Holzals Roh-und Werkstoff 64: 343-345.
Samanta, B. 2009. Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing. 22(3):257-266.
Samanta, B. 2009. Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing. 22(3):257-266.
Sarikaya, M.; Güllü, A. 2016. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL, Journal of Cleaner Production 65:604-616.
Sarikaya, M.; Güllü, A. 2016. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL, Journal of Cleaner Production 65:604-616.
Selaimia, A.A.; Yallese, M.A.; Bensouilah, H.; Meddour, I.; Khattabi, R.; Mabrouki, T. 2017. Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RSM and desirability approach. Measurement 107:53-67.
Selaimia, A.A.; Yallese, M.A.; Bensouilah, H.; Meddour, I.; Khattabi, R.; Mabrouki, T. 2017. Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RSM and desirability approach. Measurement 107:53-67.
Sofuoglu, D.S. 2017. Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (Pinus sylvestris L.) using Taguchi design method. Eur. J. Wood Prod. 75(1):33-42.
Sofuoglu, D.S. 2017. Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (Pinus sylvestris L.) using Taguchi design method. Eur. J. Wood Prod. 75(1):33-42.
Taguchi, G.; Chowdhury, S.; Wu, Y. 2005. Taguchi’s quality engineering handbook. Wiley, Hoboken.
Taguchi, G.; Chowdhury, S.; Wu, Y. 2005. Taguchi’s quality engineering handbook. Wiley, Hoboken.
Wilkowski, J.; Czarniak, P.; Grześkiewicz, M. 2011. Machinability evaluation of thermally modified wood using the Taguchi technique. In: COST Action FP0904 Workshop Mechano-chemical transformations of wood during Thermo-Hydro-Mechanical processing 109–111.
Wilkowski, J.; Czarniak, P.; Grześkiewicz, M. 2011. Machinability evaluation of thermally modified wood using the Taguchi technique. In: COST Action FP0904 Workshop Mechano-chemical transformations of wood during Thermo-Hydro-Mechanical processing 109–111.
Yang, W.H.; Tarng, Y.S. 1998. Design optimization of cutting parameters for turning operations based on the Taguchi Method. J Mater Process Technol 84:122-129.
Yang, W.H.; Tarng, Y.S. 1998. Design optimization of cutting parameters for turning operations based on the Taguchi Method. J Mater Process Technol 84:122-129.
Zhou, J.; Ren, J.; Yao, C. 2017. Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with BFR neural network and PSO algorithm. Measurement 102:271-285.
Zhou, J.; Ren, J.; Yao, C. 2017. Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with BFR neural network and PSO algorithm. Measurement 102:271-285.
Zhou, Y.; Gong, Y.; Zhu, Z.; Gao, Q; Wen, X. 2016. Modeling and optimization of surface roughness from micro grinding of nickel-based single crystal super alloy using the response surface methodology and genetic algorithm. Int Adv Manuf Technol 85:2607-2622.
Zhou, Y.; Gong, Y.; Zhu, Z.; Gao, Q; Wen, X. 2016. Modeling and optimization of surface roughness from micro grinding of nickel-based single crystal super alloy using the response surface methodology and genetic algorithm. Int Adv Manuf Technol 85:2607-2622.
Asiltürk, I.; Neseli, S.; Ince, A.M. 2016. Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement 78:120-128.
Asiltürk, I.; Neseli, S.; Ince, A.M. 2016. Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement 78:120-128.
Azhiri, R.B; Teimouri, R.; Baboly, M.G.; Leseman, Z. 2014. Application of Taguchi, ANFIS and grey relation analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Technol 71:279-295.
Azhiri, R.B; Teimouri, R.; Baboly, M.G.; Leseman, Z. 2014. Application of Taguchi, ANFIS and grey relation analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Technol 71:279-295.
Bao, X.; Ying, J.; Cheng, F.; Zhang, J.; Luo, B.; Li, L.; Liu, H. 2017. Research on neural network model of surface roughness in belt sanding process for Pinus koraiensis. Measurement 115:11-18.
Bao, X.; Ying, J.; Cheng, F.; Zhang, J.; Luo, B.; Li, L.; Liu, H. 2017. Research on neural network model of surface roughness in belt sanding process for Pinus koraiensis. Measurement 115:11-18.
Bharathi, S.R; Baskar, N. 2011. Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. The International Journal of Advanced Manufacturing Technology 54:445-463.
Bharathi, S.R; Baskar, N. 2011. Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. The International Journal of Advanced Manufacturing Technology 54:445-463.
Coelho, C.L.; Carvalho, L.M.H.; Martins, J.M.; Costa, V.A.V.; Masson, D.; Meausooner, P.J. 2008. Method for evaluating the influence of wood machining conditions on the objective characterization and subjective perception of a finished surface. Wood Sci Technol 42:181-195.
Coelho, C.L.; Carvalho, L.M.H.; Martins, J.M.; Costa, V.A.V.; Masson, D.; Meausooner, P.J. 2008. Method for evaluating the influence of wood machining conditions on the objective characterization and subjective perception of a finished surface. Wood Sci Technol 42:181-195.
Davim, J.P.; Clemente, V.C; Silva, S. 2009. Surface roughness aspects in milling MDF (medium density fiberboard). Int J Adv Manuf Technol 40:49-55.
Davim, J.P.; Clemente, V.C; Silva, S. 2009. Surface roughness aspects in milling MDF (medium density fiberboard). Int J Adv Manuf Technol 40:49-55.
Deepanraj, B.; Sivasubramanian, V.; Jayaraj, S. 2017. Multi-response optimization of process parameters in biogas production from food waste using Taguchi-Grey relational analysis. Energy Conversion and Management 14:429-438.
Deepanraj, B.; Sivasubramanian, V.; Jayaraj, S. 2017. Multi-response optimization of process parameters in biogas production from food waste using Taguchi-Grey relational analysis. Energy Conversion and Management 14:429-438.
Gaitonde, V.N; Karnik, S.R.; Davim, J.P. 2008. Taguchi multi-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept. J. Mater Process Tech 196:73-78.
Gaitonde, V.N; Karnik, S.R.; Davim, J.P. 2008. Taguchi multi-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept. J. Mater Process Tech 196:73-78.
Hazir, E.; Erdinler, E.S.; Koç, K.H. 2017. Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function. Journal of Forestry Research 1-12.
Hazir, E.; Erdinler, E.S.; Koç, K.H. 2017. Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function. Journal of Forestry Research 1-12.
Hiziroglu, S. 1996. Surface roughness analysis of wood composites: A stylus method. Forest Product Journal 46:34-42.
Hiziroglu, S. 1996. Surface roughness analysis of wood composites: A stylus method. Forest Product Journal 46:34-42.
Hiziroglu, S; Kosonkorn, P. 2006. Evaluation of surface roughness of Thai medium density fiberboard (MDF). Building and Environment 41:527-533.
Hiziroglu, S; Kosonkorn, P. 2006. Evaluation of surface roughness of Thai medium density fiberboard (MDF). Building and Environment 41:527-533.
ISO 4287. 1997. Geometrical product specifications(GPS) surface texture: profile method-terms, definitions, and surface texture profile method terms, definitions and surface texture parameters, International Organization for Standardization, Geneva
ISO 4287. 1997. Geometrical product specifications(GPS) surface texture: profile method-terms, definitions, and surface texture profile method terms, definitions and surface texture parameters, International Organization for Standardization, Geneva
Kant, G.; Sangwan, K.S. 2014. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production 83:151-164.
Kant, G.; Sangwan, K.S. 2014. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production 83:151-164.
Koç, K.H.; Erdinler, E.S.; Hazir, E.; Öztürk, E. 2017. Effect of CNC application parameters on wooden surface quality. Measurement 107:12-18.
Koç, K.H.; Erdinler, E.S.; Hazir, E.; Öztürk, E. 2017. Effect of CNC application parameters on wooden surface quality. Measurement 107:12-18.
Magoss, E. 2008.General regularities of wood surface roughness. Acta Silv. Lign. Hung.4:81-93.
Magoss, E. 2008.General regularities of wood surface roughness. Acta Silv. Lign. Hung.4:81-93.
Mahes, G.; Muthu, S.; Devadasan, S.R. 2015. Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77:369-381.
Mahes, G.; Muthu, S.; Devadasan, S.R. 2015. Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77:369-381.
Majumder, H.; Paul, T.R; Dey, V.; Dutta, P.; Saha, A. 2017. Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19-30.
Majumder, H.; Paul, T.R; Dey, V.; Dutta, P.; Saha, A. 2017. Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19-30.
Ozdemir, T; Hiziroglu, S. 2007. Evaluation of some sanding factors on the surface roughness of particleboard. Silva Fennica 41:373-378.
Ozdemir, T; Hiziroglu, S. 2007. Evaluation of some sanding factors on the surface roughness of particleboard. Silva Fennica 41:373-378.
Philbin, P; Gordon, S. 2006. Recent research on the machining of wood-based composite materials. International Journal of Machining and Machinability of Materials 1:186–201.
Philbin, P; Gordon, S. 2006. Recent research on the machining of wood-based composite materials. International Journal of Machining and Machinability of Materials 1:186–201.
Prakash, S.; Palanikumar, K. 2010. Modeling for prediction of surface roughness in drilling MDF panels using response surface methodology. J Compos Mater 45:1639-1646.
Prakash, S.; Palanikumar, K. 2010. Modeling for prediction of surface roughness in drilling MDF panels using response surface methodology. J Compos Mater 45:1639-1646.
Rao, R.V.; Kalyankar, V.D. 2013. Parameter optimization of modern machining process using teaching-learning-based optimization algorithm. Engineering Application of Artificial Intelligence 26:524-531.
Rao, R.V.; Kalyankar, V.D. 2013. Parameter optimization of modern machining process using teaching-learning-based optimization algorithm. Engineering Application of Artificial Intelligence 26:524-531.
Rao, V.K.; Murthy, N.S.G.B.P. 2016. Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf .10.1007/s10845-016-1197-y.
Rao, V.K.; Murthy, N.S.G.B.P. 2016. Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf .10.1007/s10845-016-1197-y.
Ratnasingman, J; Scholz, F. 2006. Optimal surface roughness for high-quality on Rubberwood. Holzals Roh-und Werkstoff 64: 343-345.
Ratnasingman, J; Scholz, F. 2006. Optimal surface roughness for high-quality on Rubberwood. Holzals Roh-und Werkstoff 64: 343-345.
Samanta, B. 2009. Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing. 22(3):257-266.
Samanta, B. 2009. Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing. 22(3):257-266.
Sarikaya, M.; Güllü, A. 2016. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL, Journal of Cleaner Production 65:604-616.
Sarikaya, M.; Güllü, A. 2016. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL, Journal of Cleaner Production 65:604-616.
Selaimia, A.A.; Yallese, M.A.; Bensouilah, H.; Meddour, I.; Khattabi, R.; Mabrouki, T. 2017. Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RSM and desirability approach. Measurement 107:53-67.
Selaimia, A.A.; Yallese, M.A.; Bensouilah, H.; Meddour, I.; Khattabi, R.; Mabrouki, T. 2017. Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RSM and desirability approach. Measurement 107:53-67.
Sofuoglu, D.S. 2017. Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (Pinus sylvestris L.) using Taguchi design method. Eur. J. Wood Prod. 75(1):33-42.
Sofuoglu, D.S. 2017. Determination of optimal machining parameters of massive wooden edge glued panels which is made of Scots pine (Pinus sylvestris L.) using Taguchi design method. Eur. J. Wood Prod. 75(1):33-42.
Taguchi, G.; Chowdhury, S.; Wu, Y. 2005. Taguchi’s quality engineering handbook. Wiley, Hoboken.
Taguchi, G.; Chowdhury, S.; Wu, Y. 2005. Taguchi’s quality engineering handbook. Wiley, Hoboken.
Wilkowski, J.; Czarniak, P.; Grześkiewicz, M. 2011. Machinability evaluation of thermally modified wood using the Taguchi technique. In: COST Action FP0904 Workshop Mechano-chemical transformations of wood during Thermo-Hydro-Mechanical processing 109–111.
Wilkowski, J.; Czarniak, P.; Grześkiewicz, M. 2011. Machinability evaluation of thermally modified wood using the Taguchi technique. In: COST Action FP0904 Workshop Mechano-chemical transformations of wood during Thermo-Hydro-Mechanical processing 109–111.
Yang, W.H.; Tarng, Y.S. 1998. Design optimization of cutting parameters for turning operations based on the Taguchi Method. J Mater Process Technol 84:122-129.
Yang, W.H.; Tarng, Y.S. 1998. Design optimization of cutting parameters for turning operations based on the Taguchi Method. J Mater Process Technol 84:122-129.
Zhou, J.; Ren, J.; Yao, C. 2017. Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with BFR neural network and PSO algorithm. Measurement 102:271-285.
Zhou, J.; Ren, J.; Yao, C. 2017. Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with BFR neural network and PSO algorithm. Measurement 102:271-285.
Zhou, Y.; Gong, Y.; Zhu, Z.; Gao, Q; Wen, X. 2016. Modeling and optimization of surface roughness from micro grinding of nickel-based single crystal super alloy using the response surface methodology and genetic algorithm. Int Adv Manuf Technol 85:2607-2622.
Zhou, Y.; Gong, Y.; Zhu, Z.; Gao, Q; Wen, X. 2016. Modeling and optimization of surface roughness from micro grinding of nickel-based single crystal super alloy using the response surface methodology and genetic algorithm. Int Adv Manuf Technol 85:2607-2622.
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Published
2019-10-01
How to Cite
Hazir, E., & Hüseyin Koc, K. (2019). Optimization of wood machining parameters in cnc routers: Taguchi orthogonal array based simulated angling algorithm. Maderas-Cienc Tecnol, 21(4), 493–510. Retrieved from https://revistas.ubiobio.cl/index.php/MCT/article/view/3651
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