Mechanical and thermal modeling of modulation-assisted machining

Modulation-assisted machining (MAM), which superimposes a controlled tool modulation in the feed direction of conventional machining (CM) process, can generate discrete chip formation and break the severe contact condition at the tool-chip interface. The previous literatures have shown MAM effectively reduces tool wear due to its discrete regime. However, the sources of such improvement are not well developed. This paper is dedicated to reveal the nature of MAM from the perspective of mechanical and thermal modeling. Chip formation of MAM is studied in both cylindrical turning and face turning configurations, showing that variable cutting thickness, rake angle, and flank angle are the intrinsic characteristics of MAM. Based on the chip formation analysis, an analytical force model is developed to determine the dynamic cutting force in MAM, incorporating material properties, tool geometry, cutting conditions (i.e., cutting velocity, feed rate, and depth of cut), and modulation conditions. The proposed force model is verified by orthogonal cutting experiments. Then, a thermal model is proposed to investigate the temperature variation in MAM and the effects of modulation conditions on temperature as well. The thermal model is validated by both finite element simulations and turning experiments. This paper can serve as a theoretical basis for further investigations on tool wear and surface integrity.

[1]  Mohamed A. Elbestawi,et al.  From the basic mechanics of orthogonal metal cutting toward the identification of the constitutive equation , 2002 .

[2]  V. Schulze,et al.  Simulation of Metal Cutting Process with Variable Cutting Thickness During Broaching , 2012 .

[3]  Christopher Saldana,et al.  Effects of Controlled Modulation on Interface Tribology and Deformation in Machining , 2009 .

[4]  H. Singh,et al.  Characteristic studies of brass particulates fabricated by modulation assisted machining , 2014 .

[5]  Vivek Patel,et al.  An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems , 2012 .

[6]  Yusuf Altintas,et al.  Prediction of tool and chip temperature in continuous and interrupted machining , 2002 .

[7]  M. C. Shaw.  The size effect in metal cutting , 1952, Journal of Fluids Engineering.

[8]  Christopher Saldana,et al.  Enhancing material removal processes using modulation-assisted machining , 2011 .

[9]  Yuebin Guo,et al.  A comprehensive experimental study on surface integrity by end milling Ti―6Al―4V , 2009 .

[10]  M. C. Shaw,et al.  Mechanics of Machining: An Analytical Approach to Assessing Machinability , 1989 .

[11]  Tien-Chien Jen,et al.  Nonlinear numerical analysis in transient cutting tool temperatures , 2003 .

[12]  Raja K. Kountanya,et al.  Cutting tool temperatures in interrupted cutting—The effect of feed-direction modulation , 2008 .

[13]  Binglin Li,et al.  Analytical prediction of cutting forces in orthogonal cutting using unequal division shear-zone model , 2011 .

[14]  A. Ali,et al.  Tool Temperatures in Interrupted Metal Cutting , 1992 .

[15]  M. E. Merchant.  Mechanics of the Metal Cutting Process. I. Orthogonal Cutting and a Type 2 Chip , 1945 .

[16]  M. C. Shaw.  Metal Cutting Principles , 1960 .

[17]  Zhongtao Fu,et al.  An analytical force model for ball-end milling based on a predictive machining theory considering cutter runout , 2016 .

[18]  E.J.A. Armarego,et al.  Force prediction models and CAD/CAM software for helical tooth milling processes. II. Peripheral milling operations , 1993 .

[19]  Zhongtao Fu,et al.  Analytical Modelling of Milling Forces for Helical End Milling Based on a Predictive Machining Theory , 2015 .

[20]  Yang Guo,et al.  Enhancing Tool Life in High-Speed Machining of Compacted Graphite Iron (CGI) Using Controlled Modulation , 2012, Tribology Letters.

[21]  Hisataka Tanaka,et al.  Temperature Variation in the Cutting Tool in End Milling , 2011 .

[22]  Yi Wan,et al.  Analytical modeling and experimental investigation of tool and workpiece temperatures for interrupted cutting 1045 steel by inverse heat conduction method , 2013 .

[23]  Shiv Gopal Kapoor,et al.  An Analytical Model for Prediction of Tool Temperature Fields during Continuous and Interrupted Cutting , 1994 .

[24]  Zhongtao Fu,et al.  Analytical modeling of chatter vibration in orthogonal cutting using a predictive force model , 2014 .

[25]  Yusuf Altintas,et al.  Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design , 2000 .

[26]  Volker Schulze,et al.  Surface Quality after Broaching with Variable Cutting Thickness , 2014 .

[27]  M. S. Shunmugam,et al.  Mechanistic model for prediction of cutting forces in micro end-milling and experimental comparison , 2013 .

[28]  Erhan Budak,et al.  Analytical and experimental investigation of rake contact and friction behavior in metal cutting , 2009 .

[29]  H. Meyer,et al.  Modeling the high strain rate behavior of titanium undergoing ballistic impact and penetration , 2001 .

[30]  Yang Guo,et al.  A Comparative Study of Energy and Material Flow in Modulation-Assisted Machining and Conventional Machining , 2014 .

[31]  Frederik Zanger,et al.  Numerical Investigations on Changes of the Main Shear Plane while Broaching , 2013 .

[32]  Ronglei Sun,et al.  Analysis and modeling of force in orthogonal elliptical vibration cutting , 2016 .

[33]  G. R. Johnson,et al.  Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures , 1985 .

[34]  James B. Mann,et al.  Energy dissipation in modulation assisted machining , 2013 .

[35]  Frederik Zanger,et al.  Numerical Investigation of the Changing Cutting Force Caused by the Effects of Process Machine Interaction While Broaching , 2012 .