MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING

OF THESIS MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING The use of cutting fluids in machining process is very essential for achieving desired machining performance. Due to the strict environmental protection laws now in effect, there is a wide-scale evaluation of the use of cutting fluids in machining. Consequently, minimal quantity lubrication (MQL), which uses very small quantity of cutting fluids and still offers the same functionality as flood cooling, can be considered as an alternative solution. This thesis presents an experimental study of face milling of automotive aluminum alloy A380 under four different lubrication/cooling conditions: dry cutting, flood cooling, MQL (Oil), and MQL (Water). Experiments were design using Taguchi method for design of experiments. Empirical models for predicting surface roughness and cutting forces were developed for these four conditions in terms of cutting speed, feed and depth of cut. Optimization technique using Genetic Algorithms (GA) was used to optimize performance measures under different lubrication/cooling conditions, based on a comprehensive optimization criterion integrating the effects of all major machining performance measures. Case studies are also presented for two pass face milling operation comparing flood cooling condition with MQL. The comparison of the results predicted by the models developed in this work shows that the cutting force for MQL (Oil) is either lower or equal to flood cooling. The surface roughness for MQL (Oil) is comparable to flood cooling for higher range of feed and depth of cut. A comparison of the optimized results from the case studies, based on value of utility function, shows that the optimum point for two pass face milling operation having MQL (Oil) as finish pass has highest utility function value.

[1]  Bernard Hon,et al.  Special issue on design and manufacture for sustainable development , 2004 .

[2]  Abdel E. Bayoumi,et al.  Tool wear modeling through an analytic mechanistic model of milling processes , 1992 .

[3]  Pj Hobbs.  Computers in Higher Education Economics Review , 1995 .

[4]  Shih-Chieh Lin,et al.  Force-based model for tool wear monitoring in face milling , 1995 .

[5]  G. Boothroyd,et al.  Fundamentals of machining and machine tools , 2006 .

[6]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[7]  Fathy Ismail,et al.  Surface topography characterization in finish milling , 1994 .

[8]  Huaizhong Li,et al.  Milling force prediction using a dynamic shear length model , 2002 .

[9]  Edward G. Hoffman.  Fundamentals of Tool Design , 1984 .

[10]  Álisson Rocha Machado,et al.  The effect of extremely low lubricant volumes in machining , 1997 .

[11]  Robert W. Ivester,et al.  ASSESSMENT OF MACHINING MODELS: PROGRESS REPORT , 2000 .

[12]  M. Alauddin,et al.  Computer-aided analysis of a surface-roughness model for end milling , 1995 .

[13]  I. S. Jawahir,et al.  On the interrelationships of some machinability parameters in finish turning with cermet chip forming tool inserts , 1992 .

[14]  Sean Pascoe,et al.  An overview of genetic algorithms for the solution of optimisation problems , 1999 .

[15]  P. X. Li,et al.  A New Parametric Approach for the Assessment of Comprehensive Tool Wear in Coated Grooved Tools , 1995 .

[16]  E. Armarego,et al.  The Machining of Metals , 1969 .

[17]  Richard E. DeVor,et al.  The prediction of cutting forces in end milling with application to cornering cuts , 1982 .

[18]  Brant C. White,et al.  United States patent , 1985 .

[19]  Yusuf Altintas,et al.  Mechanism of Cutting Force and Surface Generation in Dynamic Milling , 1991 .

[20]  Masami Masuko,et al.  Analytical Prediction of Three Dimensional Cutting Process—Part 1: Basic Cutting Model and Energy Approach , 1978 .

[21]  E.J.A. Armarego,et al.  AN APPRAISAL OF EMPIRICAL MODELING AND PROPRIETARY SOFTWARE DATABASES FOR PERFORMANCE PREDICTION OF MACHINING OPERATIONS , 2000 .

[22]  Generator Ground Protection.  An American National Standard , 1985 .

[23]  Shih-Chieh Lin,et al.  Tool wear monitoring in face milling using force signals , 1996 .

[24]  Bernard Hon.  Design and Manufacture for Sustainable Development 2004 , 2002 .

[25]  L. A. Kendall,et al.  An analytic mechanistic cutting force model for milling operations: a theory and methodology , 1994 .

[26]  Andrew Y. C. Nee,et al.  Theoretical modelling and simulation of cutting forces in face milling with cutter runout , 1999 .

[27]  X. Wang,et al.  Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect , 2005 .

[28]  M. Cotterell,et al.  Minimal lubrication machining of aluminium alloys , 2002 .

[29]  Tae Jo Ko,et al.  A dynamic surface roughness model for face milling , 1997 .

[30]  Richard E. DeVor,et al.  DEVELOPMENT OF MECHANISTIC MODELS FOR THE PREDICTION OF MACHINING PERFORMANCE: MODEL BUILDING METHODOLOGY , 1998 .

[31]  Kornel Ehmann,et al.  A cutting force model for face milling operations , 1993 .

[32]  John W. Sutherland,et al.  An Improved Method for Cutting Force and Surface Error Prediction in Flexible End Milling Systems , 1986 .

[33]  I. S. Jawahir,et al.  Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters , 1994 .

[34]  W. Kline,et al.  The Prediction of Surface Accuracy in End Milling , 1982 .

[35]  Jan-Eric Ståhl,et al.  Cutting force model for multi-toothed cutting processes and force measuring equipment for face milling , 1995 .

[36]  Kuang-Hua Fuh,et al.  An accuracy model for the peripheral milling of aluminum alloys using response surface design , 1997 .

[37]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[38]  Anselmo Eduardo Diniz,et al.  Using a minimum quantity of lubricant (MQL) and a diamond coated tool in the drilling of aluminum–silicon alloys , 2002 .

[39]  Hossam A. Kishawy,et al.  Effect of coolant strategy on tool performance, chip morphology and surface quality during high-speed machining of A356 aluminum alloy , 2005 .

[40]  E. Lepa,et al.  Principles of machining by cutting, abrasion and erosion , 1976 .

[41]  P.L.B. Oxley,et al.  Predicting cutting forces in face milling , 1994 .

[42]  Kuang-Hua Fuht,et al.  A Proposed statistical model for surface quality prediction in end-milling of A1 alloy , 1995 .

[43]  Ruxu Du,et al.  In-process monitoring of tool wear in milling using cutting force signature , 1991 .

[44]  Y. S. Tarng,et al.  Sensing tool breakage in face milling with a neural network , 1994 .

[45]  A. Senthil Kumar,et al.  Evaluation of Minimal of Lubricant in End Milling , 2001 .

[46]  Tae Jo Ko,et al.  Optimization of feedrate in a face milling operation using a surface roughness model , 2001 .

[47]  Richard E. DeVor,et al.  MECHANISTIC MODEL FOR THE PREDICTION OF THE FORCE SYSTEM IN FACE MILLING OPERATIONS. , 1984 .

[48]  F. W. Taylor.  The Art of Cutting Metals , 1907 .

[49]  A. S. Varadarajan,et al.  Investigations on hard turning with minimal cutting fluid application (HTMF) and its comparison with dry and wet turning , 2002 .

[50]  M. A. El Baradie,et al.  Tool-life testing in the end milling of Inconel 718 , 1995 .

[51]  Yusuf Altintas,et al.  In-Process Detection of Tool Failure in Milling Using Cutting Force Models , 1989 .

[52]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[53]  N. H. Cook,et al.  Tool Wear and Tool Life , 1973 .

[54]  J. Mccabe,et al.  Performance experience with near-dry machining of Aluminum© , 2001 .