Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results

This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniques.

[1]  Bin Jiang,et al.  Method for recognizing wave dynamics damage in high-speed milling cutter , 2017 .

[2]  D. Bajić,et al.  Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling , 2009 .

[3]  Manfred Weck,et al.  Chatter Stability of Metal Cutting and Grinding , 2004 .

[4]  Franci Cus,et al.  Optimization of cutting conditions during cutting by using neural networks , 2003 .

[5]  Concha Bielza,et al.  Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process , 2009, Expert Syst. Appl..

[6]  B. K. Vinayagam,et al.  Surface roughness prediction model using adaptive particle swarm optimization (APSO) algorithm , 2015, J. Intell. Fuzzy Syst..

[7]  J. R. Alique,et al.  Surface Roughness Modeling Based on Surface Roughness Feature Concept for High Speed Machining , 2005 .

[8]  Concha Bielza,et al.  A Bayesian network model for surface roughness prediction in the machining process , 2008, Int. J. Syst. Sci..

[9]  Kai Cheng,et al.  Dynamic cutting process modelling and its impact on the generation of surface topography and texture in nano/micro cutting , 2009 .

[10]  Maritza Correa,et al.  Modelo Pre-Proceso de predicción de la Calidad Superficial en Fresado a Alta Velocidad basado en Softcomputing , 2011 .

[11]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[12]  Tiagrajah V. Janahiraman,et al.  Modelling and Prediction of Surface Roughness and Power Consumption Using Parallel Extreme Learning Machine Based Particle Swarm Optimization , 2015 .

[13]  J. Paulo Davim,et al.  Neural network process modelling for turning of steel parts using conventional and wiper inserts , 2009 .

[14]  Xifeng Li,et al.  Prediction of cutting force for self-propelled rotary tool using artificial neural networks , 2006 .

[15]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[16]  Wisley Falco Sales,et al.  Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network , 2005 .

[17]  P SrinivasaPai PREDICTION OF SURFACE ROUGHNESS IN HIGH SPEED MACHINING: A COMPARISON , 2014 .

[18]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[19]  Raja Izamshah,et al.  Effects of End Mill Helix Angle on Accuracy for Machining Thin-Rib Aerospace Component , 2013 .

[20]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[21]  Jie Sun,et al.  Tool wear and cutting forces variation in high-speed end-milling Ti-6Al-4V alloy , 2010 .