Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

Abstract

There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials. The self propelled rotary tool (SPRT) is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk, which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece. This paper aimed to present the cutting performance of SPRT while turning hardened EN24 steel and optimize the machining conditions. Surface roughness (R _a) and metal removal rate (r _MMR) are considered as machining performance parameters to evaluate, while the horizontal inclination angle of the SPRT, depth of cut, feed rate and spindle speed are considered as process variables. Initially, design of experiments (DOEs) is employed to minimize the number of experiments. For each set of chosen process variables, the machining experiments are conducted on computer numerical control (CNC) lathe to measure the machining responses. Then, the response surface methodology (RSM) is used to establish quantitative relationships for the output responses in terms of the input variables. Analysis of variance (ANOVA) is used to check the adequacy of the model. The influence of input variables on the output responses is also determined. Consequently, these models are formulated as a multi-response optimization problem to minimize the R _a and maximize the r _MMR simultaneously. Non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive the set of Pareto-optimal solutions. The optimal results obtained through the proposed methodology are also compared with the results of validation experimental runs and good correlation is found between them.

Keywords

Self-propelled rotary turning / Empirical modeling / Response surface methodology (RSM) / Multi-objective formulation / Optimization / Non-dominated sorting genetic algorithm-II (NSGA-II)

References

[1] Kishay HA, Wilcox J. Tool wear and chip formation during hard turning with self propelled rotary tools. Int J Mach Tools Manuf, 2003, 43(4): 433-439.

[2] Dessoly V, Melkote SN, Lescalier C. Modeling and verification of cutting tool temperatures in rotary turning of hardened steel. Int J Mach Tools Manuf, 2004, 44: 1463-1470.

[3] Armarego EJA, Karri V, Smith AJR. Fundamental studies of driven and self-propelled rotary tool cutting processes—I. Theor Investig Int J Mach Tools Manuf, 1994, 34(6): 785-801.

[4] Venuvinod PK, Lau WS, Narasimha PR. Some investigations into machining with driven rotary tools. J Eng Ind, 1981, 103: 469-477.

[5] Lei ST, Liu WJ. High-speed machining of titanium alloys using the driven rotary tool. Int J Mach Tools Manuf, 2002, 42: 653-661.

[6] Li L, Kishawy HA. A model for cutting forces generated during machining with self-propelled rotary tools. Int J Mach Tools Manuf, 2006, 46(12): 1388-1394.

[7] Kishawy HA, Pang L, Balazinski M. Modeling of tool wear during hard turning with self-propelled rotary tools. Int J Mech Sci, 2011, 53(11): 1015-1021.

[8] Joshi SS, Ramakrishnan N, Nagarwalla HE, Ramakrishnan P. Wear of rotary carbide tools in machining of Al/SiCp composites. Wear, 1999, 230: 124-132.

[9] Ezugwu EO. Improvements in the machining of aero-engine alloys using self-propelled rotary tooling technique. J Mater Process Technol, 2007, 185: 60-71.

[10] Wang SH, Zhu X, Li X, Turyagyenda G (2006) Prediction of cutting force for self-propelled rotary tool using artificial neural networks. J Mater Process Technol 180:23–29

[11] Box GEP, Wilson KB. On the experimental attainment of optimum conditions (with discussion). J R Stat Soc Ser B, 1951, 13(1): 1-45.

[12] Kilickap E, Huseyinoglu M, Yardimeden A. Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol, 2011, 52: 79-88.

[13] Palanikumar K. Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology. Mater Des, 2007, 28: 2611-2618.

[14] Palanikumar K, Latha B, Senthilkumar VS, Karthikeyan R. Multiple performance optimization in machining of GFRP composites by a PCD tools using non dominated sorting genetic algorithm (NSGA-II). Met Mater Int, 2009, 15(2): 249-258.

[15] Kansal HK, Singh S, Kumar P. Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Int J Mater Process Technol, 2005, 169: 427-436.

[16] Sahin Y, Motorcu AR. Surface roughness model for machining mild steel. Mater Des, 2005, 26(4): 321-326.

[17] Ghafari S, Aziz HA, Isa MH, Zinatizadeh AA. Application of response surface methodology (RSM) to optimize coagulation–flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. J Hazard Mater, 2009, 163: 650-656.

[18] Kalyanmoy D, Amrit P, Sameer A, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm : NSGA-II. IEEE Trans Evolut Comput 6(2):182–197

[19] Montgomery DC. Design and analysis of experiments, 2001, Hoboken: Wiley

[20] Stat-Ease Inc (2001) Design-expert software, educational version 6.0.9 for windows. Wiley, New York