Prediction of Cutting Forces in Ball-End Milling of Multi-Layered Metal Materials

This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.

[1]  Behnam Bahr,et al.  An analytical method for predicting cutting forces in orthogonal machining of unidirectional composites , 2010 .

[2]  Gianni Campatelli,et al.  Improved dynamic compensation for accurate cutting force measurements in milling applications , 2015 .

[3]  Hossam A. Kishawy,et al.  Optimization of CNC ball end milling : a neural network-based model , 2005 .

[4]  S. Gangopadhyay,et al.  Comparative evaluation of machinability characteristics of Nimonic C-263 using CVD and PVD coated tools , 2016 .

[5]  Leonardo Bennun,et al.  Descriptive examples of the limitations of Artificial Neural Networks applied to the analysis of independent stochastic data , 2014, 1404.5598.

[6]  Sein Leung Soo,et al.  High performance cutting of advanced aerospace alloys and composite materials , 2015 .

[7]  U. Zuperl,et al.  A generalized neural network model of ball-end milling force system , 2006 .

[8]  Mahmoud.  Characterizations of 304 stainless steel laser cladded with titanium carbide particles , 2015 .

[9]  R. N. Rai,et al.  Study on machinability of in situ Al–4.5%Cu–TiC metal matrix composite-surface finish, cutting force prediction using ANN , 2016 .

[10]  Vimal Dhokia,et al.  Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids , 2012 .

[11]  Hou-ming Zhou,et al.  Milling Force Forecast of the Matching of Lengthened Shrink-Fit Holder and Cutter in High Speed Machining , 2010 .

[12]  Mustafa Gölcü,et al.  Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network , 2007 .

[14]  Harshit K. Dave,et al.  Modelling of cutting forces as a function of cutting parameters in milling process using regression analysis and artificial neural network , 2010 .

[15]  Klancnik.  VISION-BASED APPROACH TO END MILL TOOL MONITORING , 2015 .

[16]  Franci Cus,et al.  Simulation of cutting forces in ball-end milling , 2003 .

[17]  Franc Cus,et al.  Modeling and adaptive force control of milling by using artificial techniques , 2012, J. Intell. Manuf..

[18]  Gao Dong,et al.  Study of cutting deformation in machining nickel-based alloy Inconel 718 , 2011 .

[19]  Aitzol Lamikiz,et al.  Evaluation of the mechanical properties of Inconel 718 components built by laser cladding , 2011 .

[20]  Haider Banka,et al.  Performance evaluation of approximated artificial neural network (AANN) algorithm for reliability improvement , 2015, Appl. Soft Comput..