Neural network-based prediction for surface characteristics in CO2 laser micro-milling of glass fiber reinforced plastic composite

A novel approach to predict the surface characteristics, namely depth and surface roughness, of glass fiber reinforced plastic composite after CO2 laser milling by using artificial neural networks is developed and optimized. The experimental data are produced using a 60 W CO2 laser machine to perform milling of unidirectional glass fiber reinforced plastic composite. The CO2 laser milling is performed in both parallel and perpendicular to fiber direction at five different values of energy deposition (0.066, 0.176, 0.22, 0.264, 0.308) J/mm and three different beam diameters (225, 277.398, 463.869) μm. The artificial neural network model having the architecture of 3-6-6-3, that is two hidden layers with six neurons in each layer, is found to have the best performance based on mean error value. The mean, maximum, and minimum prediction errors for this ANN model are 0.82%, 2.26%, and 0.0004%, respectively. A semiempirical model is also developed to predict the milling depth, and its response is compared with predicted depth from neural network model. The milled depth predicted using the optimized neural network model is far superior compared to the output of the semiempirical model.

[1]  Hasan Gökkaya,et al.  A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials , 2018, Defence Technology.

[2]  Mustafa Ayyildiz,et al.  Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot , 2019, Sensor Review.

[3]  F. Kara,et al.  Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting , 2020 .

[4]  C. Sanjay,et al.  A study of surface roughness in drilling using mathematical analysis and neural networks , 2006 .

[5]  C. Leone,et al.  An experimental study on the surface mechanisms formation during the laser milling of PMMA , 2015 .

[6]  Chinmay K. Desai,et al.  Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches , 2012 .

[7]  Demeng Che,et al.  Machining of Carbon Fiber Reinforced Plastics/Polymers: A Literature Review , 2014 .

[8]  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 .

[9]  K. Singh,et al.  Laser processing of glass fiber reinforced composite material: a review , 2019 .

[10]  M. Nalbant,et al.  The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks , 2009 .

[11]  S. Sarıdemir,et al.  Prediction of performance, combustion and emission characteristics for a CI engine at varying injection pressures , 2020 .

[12]  Mustafa Ayyildiz,et al.  Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network , 2017 .

[13]  S. Joshi,et al.  Modeling of surface roughness in precision machining of metal matrix composites using ANN , 2008 .

[14]  Milan Stehlík,et al.  “SPOCU”: scaled polynomial constant unit activation function , 2020, Neural Computing and Applications.

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

[16]  Siddharth Suman.  Deep neural network based prediction of burst parameters for Zircaloy-4 fuel cladding during loss-of-coolant accident , 2020 .

[17]  Saurin Sheth,et al.  Experimental Investigation, Modelling and Comparison of Kerfwidth in Laser Cutting of GFRP , 2015 .

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

[19]  C. Sanjay,et al.  A study of surface roughness in drilling using mathematical analysis and neural networks , 2006 .

[20]  Bappa Acherjee,et al.  Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics , 2011, Appl. Soft Comput..

[21]  A. Abrão,et al.  Analysis of parametric influence on delamination in high-speed drilling of carbon fiber reinforced plastic composites , 2008 .

[22]  S. Prakash,et al.  Pulse smearing and profile generation in CO 2 laser micromachining on PMMA via raster scanning , 2018 .

[23]  Abdulrahman Al-Ahmari,et al.  Predictive machinability models for a selected hard material in turning operations , 2007 .

[24]  T. M. Yue,et al.  A study of the heat-affected zone in the UV YAG laser drilling of GFRP materials , 2002 .

[25]  S. N. Aqida,et al.  CO2 Laser Cutting of Glass Fiber Reinforce Polymer Composite , 2012 .

[26]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[27]  Saurin Sheth,et al.  Experimental Analysis and ANN Modelling of HAZ in Laser Cutting of Glass Fibre Reinforced Plastic Composites , 2016 .

[28]  J. Paruelo,et al.  How to evaluate models : Observed vs. predicted or predicted vs. observed? , 2008 .

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

[30]  Frank Schneider,et al.  High Power Laser Cutting of Fiber Reinforced Thermoplastic Polymers with cw- and Pulsed Lasers☆ , 2013 .

[31]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[32]  M. Hamedi,et al.  Combined GA-ANN approach for prediction of HAZ and bearing strength in laser drilling of GFRP composite , 2019, Optics & Laser Technology.

[33]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[34]  Islam Shyha,et al.  An Investigation into CO2 Laser Trimming of CFRP and GFRP Composites , 2013 .

[35]  W. Grzesik,et al.  Hybrid approach to surface roughness evaluation in multistage machining processes , 2003 .

[36]  Fiber laser processing of GFRP composites and multi-objective optimization of the process using response surface methodology , 2018, Journal of Composite Materials.

[37]  I. Choudhury,et al.  Experimental evaluation of laser cut quality of glass fibre reinforced plastic composite , 2013 .

[38]  M. Hamedi,et al.  Comprehensive investigation of surface quality and mechanical properties in CO2 laser drilling of GFRP composites , 2019, The International Journal of Advanced Manufacturing Technology.