Comparison of surface roughness in end milling of titanium alloy Ti-6Al-4V using uncoated WC-Co and PCD inserts through generation of models

• Main Authors: Ginta, Turnad Lenggo, Amin, A. K. M. Nurul, Ishtiyaq, M. H.
• Format: Book Chapter
• Language: English
• Published: IIUM Press 2011
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://irep.iium.edu.my/23604/4/chp25.pdf; http://irep.iium.edu.my/23604/; http://rms.research.iium.edu.my/bookstore/default.aspx
• Institution: Universiti Islam Antarabangsa Malaysia

Titanium alloys are widely known as difficult to cut materials, especially at higher cutting speeds, due to their several inherent properties. Siekmann [1] suggested that machining of titanium and its alloys would always be a problem, no matter what techniques are employed to transform this metal into chips. When machining Ti-6Al-4V, conventional tools wear rapidly because the poor thermal conductivity of titanium alloys resulting in higher cutting temperature closer to the cutting edge. There also exists strong adhesion between the tool and workpiece material [2]. Since the performance of conventional tools is poor in machining Ti-6Al-4V, a number of newly evolved tool materials, such as cubic boron nitride (CBN) and polycrystalline diamond (PCD), are being considered to achieve high-speed milling [3]. A machinability model may be defined as a functional relationship between the input of independent cutting variables (speed, feed, depth of cut) and the output known as responses (tool life, surface roughness, cutting force, etc) of a machining process [4]. Response surface methodology (RSM) is a combination of experimental and regression analysis and statistical inference. RSM is a dynamic and foremost important tool of design of experiment (DOE), wherein the relationship between response(s) of a process with its input decision variables is mapped to achieve the objective of maximization or minimization of the response properties [5-6]. Many machining researchers have used response surface methodology to design their experiments and assess results. Kaye et al [7] used response surface methodology in predicting tool flank wear using spindle speed change. A unique model has been developed which predicts tool flank wear, based on the spindle speed change, provided the initial flank wear at the beginning of the normal cutting stage is known. An empirical equation has also been derived for calculating the initial flank wear, given the speed, feed rate, depth of cut and workpiece hardness. Alauddin et al [8] applied response surface methodology to optimize the surface finish in end milling of Inconel 718 under dry condition. They developed contours to select a combination of cutting speed, and feed without increasing the surface roughness. Fuh and Wang [9] developed a model for predicting milling force model in end milling operation. They found that the proposed model is suitable for practical engineering application, since the milling force analyzed in the model has already encompassed the structural characteristics of the milling machine and the real conditions of the tool and workpiece. They also suggested that the proposed force model had a good correlation with experimental values. Choudhury and el-Baradie [10] found that response surface methodology combined with the factorial design of experiments were useful techniques for tool life testing. Relatively, a small number of designed experiments are required to generate much useful information that can be used for developing the predicting equation for tool life. Mansour and Abdalla [11] developed a surface roughness model for end milling of a semi-free cutting carbon casehardened steel. They investigated a first-order equation covering the speed range 30 – 35 m/min and a second order generation equation covering the speed range 24 – 38 m/min. They suggest that an increase in either the feed or the axial depth of cut increases the surface roughness, whilst an increase in the cutting speed decreases the surface roughness. Oktem et al [12] used response surface methodology with a developed genetic algorithm (GA) in the optimization of cutting conditions for surface roughness. S. Sharif et al [13] used factorial design coupled with response surface methodology in developing the surface roughness model in relation to the primary machining variables such as cutting speed, feed, and radial rake angle. In this paper, the RSM technique is used in developing a mathematical model to optimize the surface roughness values when end milling titanium alloy using both uncoated WC-Co and PCD inserts under dry conditions. Factorial design coupled with response surface methodology is utilized to develop the model for predicting surface roughness values.