Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083)

The quality of a machined finish plays a major role in the performance of milling operations, good surface quality can significantly improve fatigue strength, corrosion resistance, or creep behaviour as well as surface friction. In this study, the effect of cutting parameters and cutting fluid pressure on the quality measurement of the surface of the crest for threads milled during high speed milling operations has been scrutinised. Cutting fluid pressure, feed rate and spindle speed were the input parameters whilst minimising surface roughness on the crest of the thread was the target. The experimental study was designed using the Taguchi L32 array. Analysing and modelling the effective parameters were carried out using both a multi-layer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). These were shown to be highly adept for such tasks. In this paper, the analysis of surface roughness at the crest of the thread in high speed thread milling using a high accuracy optical profile-meter is an original contribution to the literature. The experimental results demonstrated that the surface quality in the crest of the thread was improved by increasing cutting speed, feed rate ranging 0.41-0.45 m/min and cutting fluid pressure ranging 2-3.5 bars. These outcomes characterised the ANN as a promising application for surface profile modelling in precision machining.

[1]  U. Natarajan,et al.  Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform , 2011 .

[2]  Ravinder Kumar,et al.  Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN) , 2015 .

[3]  V. Kuokkala,et al.  Investigation of the effect of different cutting parameters on chip formation of low-lead brass with experiments and simulations , 2013 .

[4]  Ying Li,et al.  Residual Stress, Nanohardness, and Microstructure Changes in Whirlwind Milling of GCr15 Steel , 2013 .

[5]  Nico Treurnicht,et al.  The performance of PCD tools in high-speed milling of Ti6Al4V , 2011 .

[6]  Hamza K. Akyildiz,et al.  Evaluating of cutting forces in thread machining , 2013 .

[7]  T. Moussa,et al.  Machinability characteristics of lead free-silicon brass alloys as correlated with microstructure and mechanical properties , 2012 .

[8]  M. El Badaoui,et al.  Robotic High Speed Machining of Aluminum Alloys , 2011 .

[9]  J. Skeivalas,et al.  Analysis of surface roughness parameters digital image identification , 2014 .

[10]  Guillaume Fromentin,et al.  Modeling of interferences during thread milling operation , 2010 .

[11]  Roger Serra,et al.  Detection process approach of tool wear in high speed milling , 2010 .

[12]  Ming Chen,et al.  Experimental and numerical research on the effects of minimum quantity lubrication in thread turning of free-cutting steel AISI 1215 , 2015 .

[13]  Jianfeng Li,et al.  Milling force vibration analysis in high-speed-milling titanium alloy using variable pitch angle mill , 2012 .

[14]  Arif Gok,et al.  A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA , 2015 .

[15]  C. Yue,et al.  An Investigation of Wear of Ball End Milling Cutter for High-Speed Milling of Hardened Cr12MoV Steel , 2014 .

[16]  Junyun Chen,et al.  A model for predicting surface roughness in single-point diamond turning , 2015 .

[17]  Mohammad Reza Soleymani Yazdi,et al.  Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE) , 2011 .

[18]  Jorge Salguero,et al.  Cutting Forces Parametric Model for the Dry High Speed Contour Milling of Aerospace Aluminium Alloys , 2013 .

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

[20]  Amir Mahyar Khorasani,et al.  Chatter prediction in turning process of conical workpieces by using case-based resoning (CBR) method and taguchi design of experiment , 2011 .

[21]  Guillaume Fromentin,et al.  Analytical and experimental investigations on thread milling forces in titanium alloy , 2013 .

[22]  A. Velayudham,et al.  Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach , 2010 .

[23]  C. Natarajan,et al.  Investigation of cutting parameters of surface roughness for brass using artificial neural networks in computer numerical control turning , 2012 .

[24]  M. Jackson,et al.  Microstructural damage during high-speed milling of titanium alloys , 2010 .

[25]  Raviraj Shetty,et al.  Analysis of surface roughness and hardness in ball burnishing of titanium alloy , 2014 .

[26]  Jun Zhao,et al.  Progressive tool failure in high-speed dry milling of Ti-6Al-4V alloy with coated carbide tools , 2012 .

[27]  S. Gonda,et al.  Profile surface roughness measurement using metrological atomic force microscope and uncertainty evaluation , 2015 .

[28]  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..

[29]  Şener Karabulut,et al.  Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method , 2015 .

[30]  Peng Liu,et al.  Tool Life and Surface Integrity in High-speed Milling of Titanium Alloy TA15 with PCD/PCBN Tools , 2012 .

[31]  Mohsen Marani Barzani,et al.  Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning , 2015 .

[32]  P. Y. Sevilla-Camacho,et al.  Tool breakage detection in CNC high-speed milling based in feed-motor current signals , 2011 .

[33]  Shu-quan Song,et al.  Modelling and simulation of whirling process based on equivalent cutting volume , 2014, Simul. Model. Pract. Theory.

[34]  Gilles Dessein,et al.  SUPPRESSION OF PERIOD DOUBLING CHATTER IN HIGH-SPEED MILLING BY SPINDLE SPEED VARIATION , 2011 .

[35]  Gandjar Kiswanto,et al.  The effect of spindle speed, feed-rate and machining time to the surface roughness and burr formation of Aluminum Alloy 1100 in micro-milling operation , 2014 .

[36]  J. Paulo Davim,et al.  OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS , 2012 .

[37]  Junxue Ren,et al.  Influence of high-speed milling parameter on 3D surface topography and fatigue behavior of TB6 titanium alloy , 2013 .