TAILIEUCHUNG - 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

The effect of the number of blades and the abrasive grain size of sandpaper on the surface roughness in wood materials obtained from Lombardy poplar (Populus nigra L.) and European black pine (Pinus nigra A.) were examined. A total of 220 samples were prepared for this purpose. | Turk J Agric For 29 (2005) 315-321 © TÜB‹TAK 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 Erol BURDURLU*, ‹lker USTA, Meliha ULUPINAR, Bora AKSU, T. Ça¤r› ERARSLAN Hacettepe University, Department of Wood Products Industrial Engineering, 06532 Ankara - TURKEY Received: Abstract: The effect of the number of blades and the abrasive grain size of sandpaper on the surface roughness in wood materials obtained from Lombardy poplar (Populus nigra L.) and European black pine (Pinus nigra A.) were examined. A total of 220 samples were prepared for this purpose. The samples were subjected to roughness measurements according to TS 6212 EN ISO 4288. The values obtained were statistically analyzed and the results were interpreted. The best results among the sanded surfaces in roughness were obtained with 180-grit sandpaper on tangential cut surfaces for Lombardy poplar and with 220-grit sandpaper on tangential cut surfaces for European black pine. In the planed surfaces, the best result was obtained on tangential cut surfaces with 2 blades -1 and a 5 m min feeding rate for Lombardy poplar and European black pine. Key Words: Surface roughness, Wood sanding, Wood planing, European black pine, Lombardy poplar Karaçam ve Karakavak’›n Rendelenmesi ve Z›mparalanmas›nda B›çak Say›s› ve Z›mpara Tanecik Büyüklü¤ünün Yüzey Pürüzlülü¤üne Etkisi Özet: Bu çal›flmada, Karakavak (Populus nigra L.) ve Karaçam (Pinus nigra A.) ‘dan elde edilmifl ahflap malzemenin, planya ve z›mpara makinesinde ifllenmesinde b›çak say›s› ve z›mpara tanecik büyüklü¤ünün yüzey pürüzlülü¤üne etkisi araflt›r›lm›flt›r. Bu amaçla, farkl› numaral› (Farkl› tanecik büyüklüklü) z›mparalar›n pürüzlülü¤e etkisinin belirlenebilmesi için 5 x 2 x 10 (z›mpara türü x kesifl yönü x ölçüm say›s›) = 100 ve b›çak say›s› ve besleme oran›n›n pürüzlülü¤e etkisinin belirlenmesi için 3 x 2 x 2 x 10 (b›çak say›s›

• Nông nghiệp
• Surface roughness
• Wood sanding
• Wood planing
• European black pine
• Lombardy poplar
• Response surface and artificial neural network prediction model
• Optimization for surface roughness in machining
• Optimization for surface roughness
• Response surface model
• ANN prediction model sufficiently predict
• Effects of milling conditions
• Finished surface roughness
• Four controllable factors three levels
• Performance measurements of surface roughness
• Regression of surface roughness
• The electrical discharge machining
• The EDM processes
• Machine Process Parameters
• The Surface Roughness in EDM
• Application of Taguchi on surface roughness
• Regression analysis on surface roughness
• Machining hardened AISI D2 steel
• Hardened AISI D2 steel
• Optimum parametric condition
• Taguchi design of experiment
• Desirability approach
• Energy saving and surface roughness
• Milling process parameters
• Obtain a reliable optimal solution
• Effect of coolant types on grinding force
• Coolant types on grinding force
• Surface grinding 90crsi steel
• The surface roughness significantly reduced
• Suitable cooling regimes
• The application of response surface methodology
• Predicting and optimizing surface roughness
• Cutting forces in hard turning by PVD coated insert
• PVD coated insert
• Coated ceramic insert
• Cutting forces according
• Modeling of surface roughnes
• Multi objective optimization of surface roughness
• Productivity in dry turning of AISI 52100 steel
• Coating cermet tools
• Response Surface Methodology
• Multi regression prediction model for surface roughness
• Multi regression prediction model for tool wear
• Multi regression prediction model
• Turning novel aluminum alloy (LM6)/fly ash composite
• Response surface and central composite design methodology
• Modelling of tool wear
• Analysis of tool wear
• Surface roughness in hard turning of AISI D2 steel
• The analysis of variance
• Surface roughness optimization in ball nose milling process
• Ball nose milling process of C45 steel using taguchi method
• C45 steel using taguchi method
• Ball nose mill
• Feed path to minimize the surface roughness
• Tạp chí Khoa học và Công nghệ
• Cutting power
• Dry milling
• Performance of NanoMQL technique
• Hard and soft EN31 steel
• Rresponse surface methodology
• Model of surface roughness
• MQL and NanoMQL
• DC53 die steel
• Wire EDM
• Analysis of variance
• Residual analysis
• Analysis of residuals
• Multiple regression
• Artificial bee colony algorithm
• EDM of EN31 tool steel
• Electro Discharge Machining
• Hybrid model Optimization
• Artificial neural network
• Genetic algorithm
• Electrical discharge machining
• Tool steel
• Examination of machining parameters
• EDM of tool steel
• Analytic hierarchy process
• Multicriteria decision making
• Sawing process
• Cutting phenomena
• Plasma treatment
• Sanding process
• Pinus sylvestris
• Rotary cut veneers
• Bond strength
• Methacrylate based resin composite
• Silorane based resin composite
• Adhesive system on repair potential
• Variable work piece hardness on surface roughness
• End milling using multiple regression
• Adaptive Neuro fuzzy inference systema
• CNC end milling operation
• Work piece of different hardness
• Comparison of optimization techniques for MRR
• Surface roughness in wire EDM process for gear cutting
• Wire EDM process for gear cutting
• Wire feed rate
• Pulse off time