Optimization of Boring Parameters
V Sai Nitish
Amit Shiva Kumar
UNDER THE GUIDANCE OF
Dr. BADARI NARAYANA KANTHETI
BUCKET ORIELS SPECIFICATIONS AND IMPORTANCE
• Bucket oriels are welded supports
which act as a connection between the
arm of the earth moving machine and
the bucket which carries the load.
• Bucket oriels bear huge amounts of
load, which makes it one of the most
crucial components of the earth moving
• Minute changes in diameter and surface
finish might result in excess friction,
vibrations, wear and tear.
BUCKET ORIELS ARE ATTACHED AT THE BACK OF THE
BUCKET BY METAL INERT GAS WELDING
• To obtain surface finish of the bore to be less
than 2 micro meters
• To achieve a dimensional conformance of
diameter of the hole of 70 mm.
• To find the parameters influencing the boring
• In many manufacturing organizations experiments are performed to increase the
understanding and knowledge of various manufacturing processes. For continuous
improvement in product/process quality, it is fundamental to understand the process
behaviour, the amount of variability and its impact on product / processes. The present work
being optimization of boring parameters, the literature related to this subject area is briefly
• The study of cutting forces of boring process is important for selecting appropriate proper
cutting conditions. A predictive cutting force model has been developed and influence of
cutting parameters on force components has been studied by Alwarsamy et al., 2011. In their
work a new analytical cutting force model obtain from the information related to the work
piece and tool signature for a boring operation.
• The optimization work done by Gaurav Vohra et al 2013, of the boring parameters for a CNC
turning centre such as speed, feed rate and depth of cut on aluminium to achieve the highest
possible Material removal rate and at minimum surface roughness by using the Taguchi
method. In their work it was found that the depths of cut and cutting speed to be the most
influencing parameter, followed by the feed rate.
• Kanase Sandip et al 2013 proposed an innovative method to reduce tool chatter and enhance
surface finish in boring operation. Their results showed the use of passive damping technique
has vast potential in the reduction of tool chatter.
• The optimum conditions to obtain better damping in boring process for chatter reduction
is identified for various cutting conditions by Prasanna venkadesan et al, 2015.
• Lee and Tarng 2001 used computer vision techniques to inspect surface roughness of a
work piece under a variation of turning operations and developed a relationship between
the feature of the surface image and the actual surface roughness under a variation of
turning operations for predicting surface roughness with reasonable accuracy.
• Davim 2003 studied the influence of cutting condition and cutting time on turning metal
matrix composites and used Taguchi method and (ANOVA) for the analyses. They
obtained a correlation between cutting velocity, feed and cutting time with tools wear, the
power required and the surface roughness of the job.
• Balamuruga and Sugumaran 2013 developed a Gaussian process regression model to
predict the surface roughness based on tool post vibration and cutting conditions. In their
work the data obtained from their experiments and collected from previous studies, have
been used to validate the model.
• Badadhe et. al., 2012, optimized combination of cutting parameters to achieve the
minimum value of surface roughness. In their work, the experimentation was carried out
with the help of lathe machine, with three machining parameters such as spindle speed,
feed and depth of cut taken as control factors. During the machining, the boring bar is
subjected vibrations in different directions; the impact of these vibrations on the surface
roughness which is an important quality parameter of the bored surface was estimated.
Analysis and Optimization of Parameters Affecting Surface
Roughness in a Boring Process using DOE
• Manufacturing industries are continuously demanding for
higher production rate and improved machinability as
quality and productivity play significant role in the
• Higher production rate can be achieved at high cutting
speed, feed and depth of cut
• The quality of the final product is limited by capability
of tooling and surface finish.
• Over all performance of the operation depends on the
selection of right cutting parameters and is generally a
compromise between several variables.
• Depth of cut at 1 mm (1 level)
• Feed rate is varied at 40, 80, 120, 160, 200 and 240 in mm/min ( 6
• Speed of cut 800, 900 and 1200 rpm ( 3 levels)
• The number of possible iteration using a full factorial will be 6*3 =
18. In the present program, as iterations required are small all
possible combinations are used.
• The responses measured for each iteration are:
a) surface roughness (RA) and b) hole diameter.
• To analyse the data , a statistical tool DESIGN OF
EXPERIMENTS (Mini TAB Ver 16) is used to arrive at proper
improvement plan of the manufacturing process parameters. The
experimentation plan is designed using design of experiment
• It is possible to determine these parameters using ANOVA,
Response Surface Methodologies including optimization.
A sample data of inputs and response parameters is shown below
Sl. No Factor 1
cut , mm
Speed of cut
1 1 40 800 2.320 70.025
2 1 40 900 2.306 70.010
3 1 80 900 1.618 70.015
4 1 80 1200 1.968 70.000
. . . . . .
. . . . . .
18 1 120 1200 2.306 69.999
EQUIPMENT USED FOR BORING
JIGS & FIXTURES
EQUIPMENT USED FOR BORING
AUTOMATIC TOOL CHANGER
Calibration of surface tester
Tester LC : 1 nm
MEASURING INSTRUMENT USED
A Mitutoyo SJ-201P
apparatus was used. For
each specimen three
readings were taken at
approximately 1200 angles
apart and the average value
was used for the
LC: 1 MICRON
DOE ANALYSIS IS DONE USING MINITAB VER
Steps involved in statistical analysis:
1. Statistical analysis -> DOE -> Factorials -Table of influencing factors will be
2. Statistical analysis -> ANOVA -> Balanced ANOVA
-> Main Effect plot and Interaction Effect Plot
3. Statistical analysis -> DOE -> Response Surface
-> Surface and Contour plots
4. Statistical analysis -> DOE -> Selection of optimal design
-> Response optimization
5. Model validation -> Use optimal factors, repeat the boring operation and measure
the respective hole diameter and surface roughness
Full Factorial Design
• Full factorial designs include all possible combinations of every
level of every factor.
• Full factorial designs can require a lot of trials, which can take a lot
of time and cost.
• Requires at least one observation for every combination of factors
• Allows for the measurement of all possible interactions.
• Ex1. a design of 4 factors with 3 levels each would be: 3 x 3 x 3 x 3
= 3^4 = 81 observations.
• Ex 2. 4 factors (A=3, B = 2, C=5, D= 4 levels). 3 x 2 x 5 x 4 = 120
Target value = 70.00 mm
Avg value = 70.0132 mm
In this project, we have selected the combination of optimum cutting
parameters which will result in better surface finish and diameter of the
bore. Three parameters viz. spindle speed, feed and depth of cut of the MX-5
machine were taken as control factors. The cutting trials were performed as
per full factorial method to deal with the response from multi-variables.
Carbon steel was used as a job material which was cut by using
carbide tool. DOE and Analysis of Variance (ANOVA) was carried out to
find the significant factors and their individual contribution in the response
function i.e. surface roughness and diameter accuracy of the bore.
After the validation, we have determined that the most optimal values for
our given machining conditions of speed, feed rate and depth of cut are 1000
rpm, 100 mm/min and 1 mm respectively.
Future scope of study
The selection of optimal cutting parameters to obtain specified surface roughness is very
difficult for any work piece material. In the current scenario, desired surface roughness can
be achieved by trial and error method. But it is time consuming and material is
unnecessarily wasted for this purpose. So, there is a need to find a technique that can
predict cutting parameters for the desired surface roughness.
Following are the recommendations for continuing the research work in this area.
1. Improved method for surface roughness measurement with higher sampling rate can be
helpful to locate the principal frequencies of boring vibrations in the surface roughness
2. In depth study of dynamic cutting force can be carried out regards to frequency content.
3. An intelligent machine controller can be developed as per the technique proposed in the
present work for automatic adjustment of the machining parameters.
4. Detail analysis of time delays in control action and stability analysis of the control
system can be carried out.
• S. Vetrivel et al., “Theoretical Cutting Force Prediction and Analysis of Boring Process Using
Mathcad”, World Applied Sciences Journal 12 (10): 1814-1818, 2011
• Gaurav Vohra et al., “Analysis and Optimization of Boring Process Parameters by Using
Taguchi Method”, International Journal of Computer Science and Communication
Engineering IJCSCE Special issue on “Recent Advances in Engineering & Technology”
• Kanase Sandip et. al., “Improvement Of Ra Value Of Boring Operation Using Passive
Damper”, The International Journal Of Engineering And Science (IJES), Vol. 2, Iss 7, P 103-
• V. Prasanna Venkadesan, et al., “Chatter Suppression in Boring Process Using Passive
Damper”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and
Manufacturing Engineering Vol: 9, No: 11, 2015.
• Yang WH, Tarng YS ,1998, “Design optimization of cutting parameters for turning
operations based on the Taguchi method”, J. of Materials Processing Technology 84:122-
• Davim, J.P., 2001, "A Note on the Determination of Optimal Cutting Conditions for Surface
Finish Obtained in Turning Using Design of Experiments", Journal of Materials Processing
Technology, Vol. 116, Iss. 23, pp. 305-308.
• Lee B.Y., Tarng Y.S., 2001, “Surface roughness inspection by computer vision in
turning operations”, International Journal of Machine Tools & Manufacture 41 (2001): 1251
• Davim J. P., “Design of optimization parameters for turning metal matrix composites
based on the orthogonal arrays”, Journal Processing Technology, 132, 2003, pp. 340
• Balamuruga Mohan Raj. G, Sugumaran. V, 2013, “Developing Gaussian Process Model to
Predict the Surface Roughness in Boring Operation”, International Journal of Engineering
Trends and Technology- Vol. 4 Iss.3, 2013
• A. M. Badadhe et. al., 2012, “Optimization of Cutting Parameters in Boring Operation”,
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), P 10-15.
This work has been accepted to be presented as a
paper at International Conference on Recent
Advances in Engineering Sciences (ICRAES) in
MS Ramaiah Institute of Technology, Bangalore
on 8th of September 2016.