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American J. of Engineering and Applied Sciences 3 (1): 102-108, 2010
ISSN 1941-7020
© 2010 Science Publications
Corresponding Author: Adeel H. Suhail, Department of Mechanical and Manufacturing Engineering, Faculty of Engineering,
University Putra Malaysia 43400 Serdang, Selangor, Malaysia
Optimization of Cutting Parameters Based on Surface Roughness and
Assistance of Workpiece Surface Temperature in Turning Process
Adeel H. Suhail, N. Ismail, S.V. Wong and N.A. Abdul Jalil
Department of Mechanical and Manufacturing Engineering, Faculty of Engineering,
University Putra Malaysia 43400 Serdang, Selangor, Malaysia
Abstract: Problem statement: In machining operation, the quality of surface finish is an important
requirement for many turned workpieces. Thus, the choice of optimized cutting parameters is very
important for controlling the required surface quality. Approach: The focus of present experimental
study is to optimize the cutting parameters using two performance measures, workpiece surface
temperature and surface roughness. Optimal cutting parameters for each performance measure were
obtained employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of
variance were employed to study the performance characteristics in turning operation. Results: The
experimental results showed that the workpiece surface temperature can be sensed and used
effectively as an indicator to control the cutting performance and improves the optimization process.
Conclusion: Thus, it is possible to increase machine utilization and decrease production cost in an
automated manufacturing environment.
Key words: Surface roughness, cutting temperature, Taguchi parameter design, turning
Increasing the productivity and the quality of the
machined parts are the main challenges of metal-based
industry; there has been increased interest in monitoring
all aspects of the machining process. Surface finish is
an important parameter in manufacturing engineering.
It is a characteristic that could influence the
performance of mechanical parts and the production
costs. The ratio between costs and quality of products in
each production stage has to be monitored and
immediate corrective actions have to be taken in case of
deviation from desired trend.
Surface roughness measurement presents an
important task in many engineering applications. Many
life attributes can be also determined by how well the
surface finish is maintained. Many surface roughness
prediction systems were designed using a variety of
sensors including dynamometers for force and torque
(Lin et al., 2001; Azouzi and Guillot, 1996),
accelerometers for mechanical vibrations (Abouelatta
and Madl, 2001; Choudhury and Sharat, 1993;
Jang et al., 1995; Kirby et al., 2006), acoustic emission
for (AE) sensors (Sundaram et al., 2007; Sundaram et al.,
2008; Collacott, 1975) and current probes for
current/power measurement of spindle and feed motors
(Sundaram et al., 2008). The purpose of using these
sensors in machining processes is to increase part
quality while decreasing cost and time of manufacture.
A review and more details about predicting surface
roughness in machining presented by (Benardos and
Vosniakos, 2003).
The cutting temperature is a key factor which
directly affects cutting tool wear, workpiece surface
integrity and machining precision according to the
relative motion between the tool and work piece
(Ming et al., 2003). The amount of heat generated
varies with the type of material being machined and
cutting parameters especially cutting speed which had
the most influence on the temperature (Liu et al., 2002).
Several attempts have been made to predict the
temperatures involved in the process as a function of
many parameters. Da Silva and Wallbank (1999)
presented a review for cutting temperature prediction
and measurement methods. Additionally, many
experimental methods to measure temperature directly,
only a few systems have as yet been used this
temperature as an indicator for machine performance
monitoring and for industrial application. Therefore,
design and develop control system to control the
temperature lead to better surface finish as machine
performance parameter.
Taguchi and Analysis Of Variance (ANOVA) can
conveniently optimize the cutting parameters with
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