Abstract
The tool edge radius significantly affects material deformation and flow, tool–chip friction, and a variety of machining performance measures (such as the cutting forces and tool wear) in mechanical micro/meso-scale machining. The tool edge-related research, either theoretically or experimentally, has been only focused in machining cases in which no built-up edge (BUE) is generated. To close this research gap, a comparative study of sharp and round-edge tools in orthogonal machining with BUE formation is conducted, including both experimental investigations and theoretical modeling. The experimental results show that the variations of the cutting forces are more stable in machining with a sharp tool than those in machining with a round-edge tool. A round-edge tool produces higher vibration magnitudes than does a sharp tool. The cutting vibrations do not necessarily have the same varying pattern as that of the cutting forces in machining with either a sharp tool or a round-edge tool. A neural network-based theoretical model is developed to predict three distinct regions of BUE formation (namely BUE Initiation Region, Steady BUE Region, and Unsteady BUE Region) in machining with a round-edge tool. The developed neural network model has been proven valid using a separate set of cutting experiments under different cutting conditions from those used for network training and testing.
Similar content being viewed by others
References
Zhang JZZ (2008) Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Technol 39:118–128
Rahman M, Zhou Q, Hong GS (1995) Online cutting state recognition in turning using a neural network. Int J Adv Manuf Technol 10:87–92
Sai K, Bouzid W (2005) Roughness modeling in up-face milling. Int J Adv Manuf Technol 26:324–329
Manna A (2005) Influence of machining parameters on the machinability of particulate reinforced Al/SiC–MMC. Int J Adv Manuf Technol 25:850–856
Oxley PLB (1989) The mechanics of machining: an analytical approach to assessing machinability. Ellis Horwood Limited, Chichester
Liu X, DeVor RE, Kapoor SG, Ehmann KF (2004) The mechanics of machining at the microscale: assessment of the current state of the science. Trans ASME J Manuf Sci Eng 126:666–678
Fang N, Wu Q (2009) A comparative study of the cutting forces in high speed machining of Ti-6AL-4 V and Inconel 718 with a round edge tool. J Mater Process Technol 209:4385–4438
Al-Zkeri I, Rech J, Altan T, Hamdi H, Valiorgue F (2009) Optimization of the cutting edge geometry of coated carbide tools in dry turning of steels using a finite element analysis. Mach Sci Technol 13:36–51
Ozel T (2009) Computational modelling of 3D turning: influence of edge micro-geometry on forces, stresses, friction and tool wear in PCBN tooling. J Mater Process Technol 209:5167–5177
Cheung FY, Zhou ZF, Geddam A, Li KY (2008) Cutting edge preparation using magnetic polishing and its influence on the performance of high-speed steel drills. J Mater Process Technol 208:196–204
Liu K, Li XP, Rahman M, Neo KS, Liu XD (2007) A study of the effect of tool cutting edge radius on ductile cutting of silicon wafers. Int J Adv Manuf Technol 32:631–637
Nasr MNA, Ng E-G, Elbestawi MA (2007) Modeling the effects of tool-edge radius on residual stresses when orthogonal cutting AISI 316 L. Int J Mach Tools Manuf 47:401–411
Fang N, Wu Q (2005) The effects of chamfered and honed tool edge geometry in machining of three aluminum alloys. Int J Mach Tools Manuf 45:1178–1187
Yen YC, Jain A, Altan T (2004) A finite element analysis of orthogonal machining using different tool edge geometries. J Mater Process Technol 146:72–81
Fang FZ, Zhang GX (2003) An experimental study of edge radius effect on cutting single crystal silicon. Int J Adv Manuf Technol 22:703–707
Fang N (2003) Slip-line modeling of machining with a rounded-edge tool, part I: new model and theory. J Mech Phys Solids 51:715–742
Schimmel RJ, Endres WJ, Stevenson R (2002) Application of an internally consistent material model to determine the effect of tool edge geometry in orthogonal machining. Trans ASME J Manuf Sci Eng 124:536–543
Manjunathaiah J, Endres WJ (2000) A new model and analysis of orthogonal machining with an edge-radiused tool. Trans ASME J Manuf Sci Eng 122:384–390
Trent EM, Wright PK (2000) Metal cutting, 4th edn. Butterworth Heinemann, Woburn
Takeyama H, Ono T (1968) Basic investigation of built-up edge. Trans ASME J Eng Ind 90:335–342
Gokkaya H, Taskesen A (2008) The effects of cutting speed and feed rate on BUE-BUL formation, cutting forces and surface roughness when machining Al6351 (T6) alloy. Strojniski Vestnik - J Mech Eng 54:521–530
Fang N, Dewhurst P (2005) Slip-line modeling of built-up edge formation in machining. Int J Mech Sci 47:1079–1098
Arsecularatne JA, Fowle RF, Mathew P, Oxley PLB (1996) Prediction of cutting forces and built-up edge formation conditions in machining with oblique nose radius tools. Proc Inst Mech Eng 210:457–469
Bao H, Stevenson MG (1976) An investigation of built-up edge formation in the machining of aluminum. Int J Mach Tools Manuf 16:165–178
Shigeo Z, Okazaki T (1970) Influences of cutting speed on built-up edges in machining of aluminum alloy. Bulletin JSME 13:724–728
Hansan SZ (1967) Relationship between the initiation of built-up edge and tool forces using the lathe. Ph.D. Dissertation, Washington University
Nakayama K, Shaw MC, Brewer RC (1966) Relationship between cutting forces, temperatures, built-up edge and surface finish. Annals CIRP 14:211–223
Venkatesh VC, Xue W (1996) A study of the built-up edge in drilling with indexable-coated carbide inserts. J Mater Process Technol 58:379–384
Dolodarenko AG, Ham I (1976) Effects of built-up edge in drilling. Trans ASME J Eng Ind 98:287–292
Ramaswami R (1974) The effect of built-up edge on surface finish, vibration and tool wear during orthogonal turning. Microtecnic 28:221–227
Kuznetsov VD (1966) Metal transfer and build-up in friction and cutting. Headington Hill Hall, Pergamon, Oxford
Fang N, Wu Q, Mosquea S (2009) An experimental comparative study of chip morphology, chip flow speed, and cutting forces in high speed machining of Ti-6AL-4 V and Inconel 718. Trans NAMRI/SME 37:97–104
Wu Q, Fang N (2006) Effect of tool-edge wear in high-speed machining of superalloy Inconel 718. Trans NAMRI/SME 34:397–402
Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press, Cambridge
Srinivasa PP, Ramakrishna Rao PK, Ramachandra A, Mathew TM, Ghanaraja S (2000) Tool condition monitoring in face milling using surface roughness and vibration analysis techniques. Proc Nat Sem Fut Trends Mech Eng Res & Dev, Roorkee, India, September 29–30
Ezugwu EO, Fadare DA, Bonney J, Da Silva RB, Sales WF (2005) Modeling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45:1375–1385
Yigit K, Ozel T (2004) Hard turning optimization using neural network modeling and swarm intelligence. Trans NAMRI/SME 33:179–186
Dimla DE Jr, Lister PM, Leighton NJ (1997) Neural network solution to the tool condition monitoring problem in metal cutting—a critical review of methods. Int J Mach Tools Manuf 37:1219–1241
Haykin S (1998) Neural networks: a comprehensive foundation. Macmilian College, New York
Leem CS, Dornfeld DA, Dreyfus SE (1997) A customized neural network for sensor fusion in on-line monitoring of cutting tool wear. Trans ASME J Eng Ind 117:152–159
Leustean L (2001) Liquid flow time series prediction using feed-forward neural networks and Rprop learning algorithm. Stud Inform Control 10:287–299
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fang, N., Pai, P.S. & Mosquea, S. A comparative study of sharp and round-edge tools in machining with built-up edge formation: cutting forces, cutting vibrations, and neural network modeling. Int J Adv Manuf Technol 53, 899–910 (2011). https://doi.org/10.1007/s00170-010-2887-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-010-2887-9