Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function

In this study, 25 (five factors at two-level factorial design) design of experiment was applied to investigate a set of optimal machining parameters to achieve a minimum surface roughness value for Abies nordmanniana. Wood specimens were prepared using different values of spindle speed, feed rate, depth of cut, tool radius, and cutting directions. Average surface roughness $$ \left( {R_{z} } \right) $$Rz values were applied using a stylus. The objectives were to: (1) obtain the effective variables of wood surface roughness; (2) analyze which of these factors had an impact on variability in the CNC machining process; (3) evaluate the optimal cutting values within the range of different cutting levels of machining parameters. The results indicate that the design of experiment (DOE) based on the desirability function approach determined the optimal machining parameters successfully, leading to minimum Ra compared to the observed value. Minimum surface roughness values of tangential and radial cutting directions were 3.58 and 3.21 µm, respectively.

[1]  Klaus Hinkelmann,et al.  Design and Analysis of Experiment , 1975 .

[2]  R. Gunst.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[3]  S. Hiziroglu.  Surfact roughness analysis of wood composites : A stylus method , 1996 .

[4]  Andres L. Carrano,et al.  Quantification of process parameters in a wood sanding operation. , 1999 .

[5]  J. V. Grice,et al.  Design and Analysis of Experiment , 2000, Technometrics.

[6]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[7]  Janez Kopac,et al.  Wood: an important material in manufacturing technology , 2003 .

[8]  C. Tanaka,et al.  Evaluation of surface smoothness by laser displacement sensor 1: effect of wood species , 2003, Journal of Wood Science.

[9]  M. Irle,et al.  Processing roughness of sanded wood surfaces , 2005, Holz als Roh- und Werkstoff.

[10]  Takeshi Ohuchi,et al.  Milling of wood and wood-based materials with a computerized numerically controlled router V: development of adaptive control grooving system corresponding to progression of tool wear , 2005, Journal of Wood Science.

[11]  Carlos A. V. Costa,et al.  Method for evaluating the influence of wood machining conditions on the objective characterization and subjective perception of a finished surface , 2008, Wood Science and Technology.

[12]  K. Palanikumar,et al.  SURFACE ROUGHNESS PARAMETERS OPTIMIZATION IN MACHINING A356/SiC/20p METAL MATRIX COMPOSITES BY PCD TOOL USING RESPONSE SURFACE METHODOLOGY AND DESIRABILITY FUNCTION , 2008 .

[13]  J. Paulo Davim,et al.  Taguchi multiple-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept , 2008 .

[14]  Chen Lu,et al.  Study on prediction of surface quality in machining process , 2008 .

[15]  J. Paulo Davim,et al.  Surface roughness aspects in milling MDF (medium density fibreboard) , 2009 .

[16]  Safian Sharif,et al.  Roughness models for sanded wood surfaces , 2010, Wood Science and Technology.

[17]  K. Palanikumar,et al.  Modeling for prediction of surface roughness in drilling MDF panels using response surface methodology , 2011 .

[18]  N. Baskar,et al.  Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation , 2011 .

[19]  Julie Cool,et al.  Improving the Sanding Process of Black Spruce Wood for Surface Quality and Water-Based Coating Adhesion , 2011 .

[20]  A. Act,et al.  EFFECT OF MACHINING PARAMETERS ON SURFACE QUALITY AFTER FACE MILLING OF MDF , 2012 .

[21]  Safian Sharif,et al.  Mathematical Modeling of Cutting Force in Milling of Medium Density Fibreboard Using Response Surface Method , 2012 .

[22]  A. Sütçü.  Investigation of Parameters Affecting Surface Roughness in CNC Routing Operation on Wooden EGP , 2012 .

[23]  Z. Zhong,et al.  Measurement of the surface roughness of wood based materials used in furniture manufacture , 2013 .

[24]  Nikolaos A. Fountas,et al.  CNC micromilling properties and optimization using genetic algorithms , 2014 .

[25]  M. Alves,et al.  The quality of MDF workpieces machined in CNC milling machine in cutting speeds, feedrate, and depth of cut , 2015 .

[26]  D. Bingöl,et al.  Full Factorial Design Approach to Hg(II) Adsorption onto Hydrogels , 2015 .

[27]  S. Sofuoglu.  Determination of Optimal Machining Parameters of Massive Wooden Edge-Glued Panels Made of European larch (Larix decidua Mill.) using Taguchi Design Method , 2015 .

[28]  A. Temiz,et al.  Effect of Wood Preservatives on Surface Properties of Coated Wood , 2015 .

[29]  M. M. Saleem,et al.  Design of experiments based factorial design and response surface methodology for MEMS optimization , 2015 .

[30]  S. Hiziroglu,et al.  Adhesion Strength of Cellulosic Varnish Coated Wood Species as Function of Their Surface Roughness , 2015 .

[31]  R. Bhalamurugan,et al.  Performance Characteristic Analysis of Automated Robot Spray Painting Using Taguchi Method and Gray Relational Analysis , 2015 .

[32]  R. Banerjee,et al.  Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. , 2016, Bioresource technology.

[33]  İlhan Asiltürk,et al.  Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods , 2016 .