Design of experiments and focused grid search for neural network parameter optimization

The present work offers some contributions to the area of surface roughness modeling by Artificial Neural Networks (ANNs) in machining processes. It proposes a method for an optimized project of a Multi-Layer Perceptron (MLP) network architecture applied for the prediction of Average Surface Roughness (Ra). The tuning method is expressed in the format of an algorithm employing two techniques from Design of Experiments (DOE) methodology: Full factorials and Evolutionary Operations (EVOP). Datasets retrieved from literature are employed to form training and test data sets for the ANN. The proposed tuning method leads to significant reduction of roughness prediction errors in machining operations in comparison to techniques currently used. It constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature on this subject. We propose a systematic approach to design and optimize MLP networks.We used DOE, Evolutionary Operation and Focused Grid Search for optimization.The proposed method is compared to previous studies in machining applications.The method presents superior results for all the comparisons.

[1]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[2]  Joseph G. Pigeon,et al.  Statistics for Experimenters: Design, Innovation and Discovery , 2006, Technometrics.

[3]  Terry Speed.  Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.) , 2006 .

[4]  Sami Ekici,et al.  Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel , 2012, J. Intell. Manuf..

[5]  Ramón Quiza,et al.  Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel , 2008 .

[6]  Surjya K. Pal,et al.  Surface roughness prediction in turning using artificial neural network , 2005, Neural Computing & Applications.

[7]  Zhaowei Zhong,et al.  Prediction of surface roughness of turned surfaces using neural networks , 2006 .

[8]  Theresa L. Utlaut,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[9]  Pedro Paulo Balestrassi,et al.  Design of experiments on neural network's training for nonlinear time series forecasting , 2009, Neurocomputing.

[10]  Wisley Falco Sales,et al.  Influence of machining parameters on fatigue endurance limit of AISI 4140 steel , 2008 .

[11]  Pedro Paulo Balestrassi,et al.  Artificial neural networks for machining processes surface roughness modeling , 2010 .

[12]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[13]  Ulaş Çaydaş,et al.  A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method , 2008 .

[14]  J. Paulo Davim,et al.  Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models , 2008 .

[15]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[16]  Durmus Karayel,et al.  Prediction and control of surface roughness in CNC lathe using artificial neural network , 2009 .

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

[18]  F. Erzincanli,et al.  Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm , 2006 .

[19]  J. Lima,et al.  Simulating Electricity Spot Prices in Brazil Using Neural Network and Design of Experiments , 2007, 2007 IEEE Lausanne Power Tech.

[20]  José R. Dorronsoro,et al.  Finding optimal model parameters by deterministic and annealed focused grid search , 2009, Neurocomputing.

[21]  Parag Vichare,et al.  Surface roughness prediction model for CNC machining of polypropylene , 2008 .

[22]  P. V. Rao,et al.  A surface roughness prediction model for hard turning process , 2007 .

[23]  Seung-Han Yang,et al.  Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm , 2010 .

[24]  P. J. García Nieto,et al.  The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process , 2010, Math. Comput. Model..

[25]  Chung-Feng Jeffrey Kuo,et al.  Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends , 2006 .

[26]  M. R. Martinez-Blanco,et al.  Robust Design of Artificial Neural Networks Applying the Taguchi methodology and DoE , 2006, Electronics, Robotics and Automotive Mechanics Conference (CERMA'06).

[27]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[28]  V. L. Anderson.  Evolutionary Operation : A Method for Increasing Industrial Productivity , 1970 .

[29]  Teresa Bernarda Ludermir,et al.  Design of experiments in neuro-fuzzy systems , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[30]  Tuğrul Özel,et al.  Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization , 2007 .

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

[32]  Ahmet Yardimeden,et al.  Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm , 2011 .

[33]  Paul G. Maropoulos,et al.  Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351 , 2010 .

[34]  Habibollah Haron,et al.  Prediction of surface roughness in the end milling machining using Artificial Neural Network , 2010, Expert Syst. Appl..

[35]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[36]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[37]  Jack P. C. Kleijnen,et al.  State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments , 2005, INFORMS J. Comput..

[38]  John H. Sheesley,et al.  Quality Engineering in Production Systems , 1988 .

[39]  Franci Cus,et al.  Approach to optimization of cutting conditions by using artificial neural networks , 2006 .

[40]  Douglas C. Montgomery,et al.  A systematic approach to planning for a designed industrial experiment , 1993 .

[41]  Vishal S. Sharma,et al.  Estimation of cutting forces and surface roughness for hard turning using neural networks , 2008, J. Intell. Manuf..

[42]  Bijoy Bhattacharyya,et al.  Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model , 2006 .

[43]  João Paulo Davim,et al.  A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling , 2008 .

[44]  Cristiano Cervellera,et al.  Neural network and regression spline value function approximations for stochastic dynamic programming , 2007, Comput. Oper. Res..

[45]  Eyup Bagci,et al.  Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN , 2006 .

[46]  Teresa B Ludermir,et al.  Global Optimization Methods for Designing and Training Feedforward Artificial Neural Networks , 2007 .

[47]  Wen-Chin Chen,et al.  A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN , 2010, Expert Syst. Appl..

[48]  Ching-Kao Chang,et al.  Study on the prediction model of surface roughness for side milling operations , 2006 .

[49]  Manoj Kumar Tiwari,et al.  Multiple response optimization using Taguchi methodology and neuro‐fuzzy based model , 2006 .