Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool
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Kishay HA, Wilcox J (2003) Tool wear and chip formation during hard turning with self propelled rotary tools. Int J Mach Tools Manuf 43(4):433–439
Dessoly V, Melkote SN, Lescalier C (2004) Modeling and verification of cutting tool temperatures in rotary turning of hardened steel. Int J Mach Tools Manuf 44:1463–1470
Armarego EJA, Karri V, Smith AJR (1994) Fundamental studies of driven and self-propelled rotary tool cutting processes—I. Theor Investig Int J Mach Tools Manuf 34(6):785–801
Venuvinod PK, Lau WS, Narasimha RP (1981) Some investigations into machining with driven rotary tools. J Eng Ind 103:469–477
Lei ST, Liu WJ (2002) High-speed machining of titanium alloys using the driven rotary tool. Int J Mach Tools Manuf 42:653–661
Li L, Kishawy HA (2006) A model for cutting forces generated during machining with self-propelled rotary tools. Int J Mach Tools Manuf 46(12):1388–1394
Kishawy HA, Pang L, Balazinski M (2011) Modeling of tool wear during hard turning with self-propelled rotary tools. Int J Mech Sci 53(11):1015–1021
Joshi SS, Ramakrishnan N, Nagarwalla HE, Ramakrishnan P (1999) Wear of rotary carbide tools in machining of Al/SiCp composites. Wear 230:124–132
Ezugwu EO (2007) Improvements in the machining of aero-engine alloys using self-propelled rotary tooling technique. J Mater Process Technol 185:60–71
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
Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions (with discussion). J R Stat Soc Ser B 13(1):1–45
Kilickap E, Huseyinoglu M, Yardimeden A (2011) Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 52:79–88
Palanikumar K (2007) Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology. Mater Des 28:2611–2618
Palanikumar K, Latha B, Senthilkumar VS, Karthikeyan R (2009) Multiple performance optimization in machining of GFRP composites by a PCD tools using non dominated sorting genetic algorithm (NSGA-II). Met Mater Int 15(2):249–258
Kansal HK, Singh S, Kumar P (2005) Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Int J Mater Process Technol 169:427–436
Sahin Y, Motorcu AR (2005) Surface roughness model for machining mild steel. Mater Des 26(4):321–326
Ghafari S, Aziz HA, Isa MH, Zinatizadeh AA (2009) Application of response surface methodology (RSM) to optimize coagulation–flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. J Hazard Mater 163:650–656
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
Montgomery DC (2001) Design and analysis of experiments. Wiley, Hoboken
Stat-Ease Inc (2001) Design-expert software, educational version 6.0.9 for windows. Wiley, New York