2. Raj Mohan B V S and Suresh R K
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1. INTRODUCTION
Today, Quality Responsive Excellence is the most important business objective
(compared to earlier philosophy of manufacturing high quality product at optimal cost
that justifies the price of the product). The purpose of undertaking Cutting Parameter
Optimization in Turning is two-fold: Quality and Economics.
Turning is a widely used Industrial Manufacturing process for circular cross-
section components. A CNC Turn Center or a Conventional Lathe (like Engine lathe)
is used depending on market (external customers), manufacturing demands, enterprise
skills and monetary resources. A Manufacturing process involves a number of process
parameters (controllable and uncontrollable) which affect the output (response
variable). Turning Cutting Parameters (input cutting parameters eg: cutting speed,
feed rate, depth of cut, coolant, material and geometry of insert used for cutting) have
a bearing on Quality, Time and Cost (QTC) of the resultant finished component (for a
given work material grade). First the objectives (Response) have to be written down,
followed by control factors and their bounds, practical constraints of machining.
Enquiring Experienced Industrial Engineers and Machine Operators/ using Machining
Data Handbooks/ trial machining have to be performed for suitable choice of cutting
parameters. Cutting speed is the major factor to be controlled during machining as it
not only determines the production rate but also the tool life. After finding limits of
cutting speed, applicable feeds must be calculated (feed depends on tool (insert) nose
radius). Depth of cut should be adjusted for good cut. Choose Feed & Depth of cut as
appropriate to the cutting material and set the parameters observing the resultant
chips. Cutting insert (material and geometry) should be chosen based on applicability
to work material (hardness and other criteria). An excessively large nose radius of
insert can lead to vibration tendencies. For roughing, use larger nose radius (example:
rE = 1.2 mm) and for finishing use nose radius of 0.8 mm or 1.2 mm.
The basic idea of Optimization is to increase quality and reduce cost and find the
optimal set of input parameters. Optimal Turning Cutting Parameters are usually
picked by an experienced human technician (Process Planner or Machine Operator)
driven by wisdom (success or failure in component machining), combination of
material and size of work, material and size of cutter, available range of machine
variables and machine accuracy limitations and data from standard Industrial
Handbooks. Reduced Manufacturing Time, Good Tool Life and Good Surface Finish
(in finishing operation) are also important considerations. Coolant has been used in
the current machining (i.e, wet turning) as coolant is a must from friction heat
removal and tool life criteria. Coolant reduces heat considerably and has added
benefits (like reduction of BUE- Built Up Edge). The Objective of this work is to
obtain the optimal combination of the selected controllable input cutting parameters
namely cutting speed, feed rate, depth of cut and nose radius of insert edge for the
Objective Function comprising of the weighted summation of chosen group of
response characteristics [Surface Roughness (SR) and Material Removal Rate
(MRR)]. As the number of objectives (response characteristics) is two, this is termed
a Multi Objective Optimization problem (as opposed to Single Objective
Optimization wherein there is only one objective). The target value Surface
Roughness is also a goal to achieve. The aim is to determine the BEST AND
PRACTICALLY FEASIBLE COMBINATION of {cutting speed, feed rate, depth
of cut, nose radius} which gives optimal {SR, MRR}. The Research Procedure
uses Ant Colony Optimization (ACO) to get the response characteristics. For MRR,
the Regression of values obtained by formula is used; and for SR, the Regression of
3. Multi Objective Optimization of Cutting Parameters during Turning of EN31 Alloy Steel
using Ant Colony Optimization
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Experimental values is used to get the objective function utilized in the Algorithms.
The optimal set of the response characteristics obtained from the algorithm is
necessarily compared against actual results of turning on the CNC Turning Center.
SR and MRR are considered one at a time to find ideal conditions when only one
Objective is of interest.
2. LITERATURE SURVEY
Literature Survey is an important part of any Research Project where latest knowledge
and contributions of researchers are used as a starting point.
Panda A et al [1] found that Feed is the most dominant parameter affecting the
surface roughness. In this paper, Taguchi technique has been utilized for parametric
optimization of surface roughness. Abhang et L B al [2] utilize the regression
modeling in turning process of EN-31 steel using response surface methodology
(RSM) with factorial design of experiments. From the analysis, it is observed that
feed rate is the most significant factor on the surface roughness followed by cutting
speed and depth of cut at 95% confidence level. Kalyanmoy Deb and Rituparna Datta
[3] describe the use of an evolutionary multi-objective optimization (EMO) algorithm
and a suitable local search procedure to optimize the machining parameters (cutting
speed, feed rate, and depth of cut) in turning operations. EMO solutions are modified
using a local search procedure to achieve a better convergence property. Meenu Sahu
and Komesh Sahu [4] present an Optimization method of the cutting parameters
(cutting speed, depth of cut and feed) in dry turning of AISI D2 Steel to achieve
minimum tool wear, low workpiece surface temperature and maximum material
removal rate. Suresh R K and Krishnaiah G [5] investigate and obtain an optimal
setting of process parameters in turning for maximizing the material removal rate of
the manufactured component. The result of this analysis identifies the optimal values
of process parameters for effective and efficient machining. Dorigo M, Maniezzo V &
Colorni A [6] describe the Ant System type of ACO implementation and the features
of the ACO algorithm. The procedure, utility and the steps to be followed are
described. Wang J et al [7] research the economic benefits of using optimized cutting
parameters. Maximum production criteria are chosen and a numerical study is carried
out that verifies optimization strategies and shows economic benefits of using
optimization of cutting parameters. Suresh R K et al [8] describe the effect of cutting
parameters on Surface roughness and material removal, interface temperature and
flank wear. The optimal turning parameters are determined by composite desirability
index and grey relational grade. Analysis of variance (ANOVA) is used to determine
the influence of parameters which significantly affect the responses simultaneously
3.RESPONSE CHARACTERISTICS
Information regarding Response Characteristics and their importance follows. The
following two response Characteristics were chosen.
3.1Surface Roughness (SR)
To measure Cutting Quality, Surface Roughness parameter is measured. In many
cases, Surface Roughness is an important external customer requirement.
Irregularities produced by cutting tool are referred to as Surface Roughness (in
machined surfaces) [11]. Characteristic evidence in the form of finely spaced micro
irregularities is left by the cutting tool. Each type of cutting tool leaves its own
individual pattern which therefore can be identified. This pattern is known as surface
4. Raj Mohan B V S and Suresh R K
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finish or surface roughness. It is well known that fn and rE affect SR. Surface
Roughness is directly proportional to square of fn, feed rate (mm/ rev) and inversely
proportional to rE, nose radius (mm) of insert edge.SR α
Therefore the parameter limits for fn and rE have to be set in fashion that the target
of low surface roughness is met. Factors that determine Surface Roughness in
machining: Three main factors determine Surface Roughness. F1. Rate of Feed F2.
Built Up Edge F3. Vibrational Deflections (termed chatter) between tool and the
work. In this study, a Regression Model is developed for SR based on the Expt Data.
Regression Models are mathematical estimation equations with response variable as a
function of process parameters. These models are developed statistically by utilizing
the information of the measured response variable and the corresponding design
matrix. Figure of cause effect diagram for Surface Roughness (Ra) follows.
Figure 1 Cause Effect Diagram vParameters that affect Ra - Benardos & Vosnaikos (2003)
Ra, Arithmetic Average Roughness: If measurement is not done, Quality
Control (QC) cannot be achieved. Ra is one of the widely used measures of Surface
Roughness.
Ra is the arithmetic average of the absolute values of the profile height deviations
from the mean line, recorded within the evaluation length (as described in ASME
B46.1 standard for surface roughness). Arithmetic average roughness, or Ra, is the
arithmetic average height of roughness-component irregularities (peak heights and
valleys) from the mean line, measured within the sampling length, L. Lower value of
Ra indicates a smooth finish.
Ra = (
L = evaluation length (sampling length)
Z(x) = the profile height function
SR (by Regression) = [0.165 – (0.00017. cutting speed) + (12.69. feed rate)
+ (0.069. depth of cut) – (1.057. nose radius)]
3.2 Material Removal Rate (MRR)
Material Removal Rate (abbreviated MRR) is a measure of how fast the material is
being removed. MRR using volume of material removed (mm3
/ min) is also called
Volumetric Material Removal Rate (VMRR).
This is to distinguish it from another representation of MRR as mass of material
removed per time (kg/ min). In the industries, the maximum production rate is
desirable and is usually achieved by selecting a cutting speed that best balances
5. Multi Objective Optimization of Cutting Parameters during Turning of EN31 Alloy Steel
using Ant Colony Optimization
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material removal rate and tool life. In rough turning operations, the aim is to
maximize MRR. MRR directly affects cycle time.
Equation for Material Removal Rate: By definition, MRR is (Volume of Material
Removed/ cutting time).
MRR = ( π. Davg . ap . fn. N ) mm3
/ min
Using, Depth of Cut, ap =
N = spindle rpm, fn = Feed Rate, D1 =Diameter before turning;
D2 = Diameter after turning, Therfore, Davg =
The formula for MRR used in the current study is
MRR = mm3
/ min
The data obtained from the above equation is fitted using Regression to obtain the
Regression Equation for MRR
MRR (by Regression) = [-27806 + (89.64. cutting speed) + (111064. feed rate)
+ (17908. depth of cut) + (109. nose radius)]
4. EXPERIMENTAL PROCEDURE
First, basic data of Machine, Materials and Instrumentation is collected. Thereafter,
based on the collected data and available parameter levels, the Experiment is designed
using a DOE Software Tool (Minitab).
4.1 Machine, Materials, Instrumentation
The details of Machine, Materials and Instrumentation follow.
4.1.1 MACHINE: The machine is DMG CTX 310 eco (shown in figure 2)
CNC Turning Center @ M/s Uma Engineering, Bengaluru, Karnataka
4.1.2 MATERIALS
The details of Work Material and Tool Material are listed.
4.1.2.1 Work Material
Work Material = Alloy Steel EN 31 (shown in figure 3) The Work Material is
EN31 High Carbon Alloy Steel. This is widely used in the Indian market for
applications like bearings and dies.
Applications of EN31: Taps, Gauges, Ejector pins, Ball and Roller bearings,
Spinning Tools, Beading Rolls, Punches and Dies, Swaging Dies
Table 1 Chemical Composition of EN31 Alloy Steel
C Mn Si S P Cr
Material
EN 31
0.9
to
1.2
0.3
to
0.75
0.1
to
0.35
0.040 0.040
1.0
to
1.6
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4.1.2.2 Tool Material
Figure.6 Figure shows the five parameters that describe the DNMG Insert {r, d, l, s, d1}
The Tool is DNMG Carbide Inserts (shown in figure 4 and figure 6) (Geometry:
55° Diamond). Ceratizit (make) - CTP 5115 (grade) - PVD coated (coating) - Nose
Radius = {0.4, 0.8, 1.2} (mm). DNMG Carbide Inserts: Carbide Inserts are a type
of cutter inserts. Different insert shapes accomplish specific types of machining
operations on various workpiece shapes. DNMG refers to the Geometry. The
Geometry is 55° Diamond. “D” , the first letter in DNMG is the designation used for
Nose Angle = 55° Diamond Shape insert. Inserts which have a smaller nose angle are
weaker, and the inserts with wider nose angle have more strength. The above figure
shows the parameters of a DNMG insert.
Machine
CNC Turning Center = DMG CTX 310 eco
Figure 2 CNC Machine - DMG CTX 310 eco
Material
Work Material = Alloy Steel EN31
Figure 3 Machined Work-pieces - EN31
Tool Material = DNMG Carbide Inserts -
Ceratizit Make
Figure 4 DNMG Insert (DNMG is a type of
Carbide Coated Insert)
Instrumentation for Surface
Roughness
Figure 5 Form Taly Surf Series 2
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4.1.3 INSTRUMENTATION FOR SURFACE ROUGHNESS
The Instrument used for Surface Roughness is Form Taly Surf Series 2 (shown in
figure 5). Form Taly Surf Series 2. Taylor Hobson Pneumo (make). Pickup – 2 µm
Radius Diamond Tip
5. OPTIMIZATION PROBLEM
The details of input and output parameters, bounds and constraints are first listed. The
Objectives and Objective function are later presented.
5.1 Selected Cutting Parameters. The 4 Selected Cutting Parameters
(Controllable Input Parameters)
cutting speed v m/ min
feed rate fn mm/ rev
depth of cut ap mm
nose radius of insert rE mm
5.2 Selected Response Characteristics. The 2 Selected Response
Characteristics (Output Parameters)
Surface Roughness SR µm
Material Removal Rate MRR mm3
/ min
Naturally, the Response Characteristics are expressed as a function of the selected
input parameters
Surface Roughness (SR) = f1(v, fn, ap, rE)
Material Removal Rate (MRR) = f2(v, fn, ap, rE)
Need to include Surface Roughness as a Response Characteristic: Finish is a
functional necessity (friction, aesthetics etc) and an important customer requirement.
Therefore it is necessary to include SR as a response characteristic.
Need to include Material Removal Rate as a Response characteristic: MRR
directly affects cycle time, an important parameter, hence MRR is included.
5.3 Bounds of Input Variables
B1. 100 <= v <= 200 where v = cutting speed, m/ min
B2. 0.1 <= fn <= 0.15 where fn = feed rate, mm/ rev
B3. 0.4 <= ap <= 1.2 where ap = depth of cut, mm
B4. 0.4 <= rE <= 1.2 where rE = nose radius, mm
Nose radius of cutters available in tool market is available only at rE values like
0.4 mm, 0.8mm, 1.2 mm and not random user desired values, and therefore there is a
need to round off algorithm output to a practical and nearest available rE
5.4 Constraints
Implied constraints like Allowable Maximum Power Consumption and Allowable
Maximum Cutting Force exist
Constraint 1. Power Consumption
Constraint 2. Cutting Force
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5.5 OBJECTIVES
Objective 1.Low Surface Roughness is desirable
Objective 2.High Material Removal Rate is desirable
5.5.1 OBJECTIVE FUNCTION
Multi Objective Optimization (as opposed to single Objective Optimization) deals
with multiple and sometimes conflicting objectives that have to be simultaneously
achieved. Scalarization Technique to solve an MOO Problem: A multi-objective
problem is often solved by combining its multiple objectives into one single-objective
scalar function (Weighted Sum approach).
Objective Function for the current study:
Minimum surface roughness and maximum MRR are desirable. Therefore the
Objective Function is formulated as under:
To Minimize the objective function z
Minimize. z = w1. (SR) + w2. (-MRR)
= w1. f1(v,f,ap, rE) + w2. - f2 (v,f,ap, rE)
Where: w1, w2 are weights given to the Response Characteristics based on actual/
desired/ chosen relative importance by Decision Maker (DM).The end user (DM)
usually decides the Numerical Values to be used for the weights w1 and w2. The end
user (DM) can provide specific values depending on the functionality core
requirements and relative importance to one or more response variables [a freedom to
decide (n-1) weights] desired at his end. Relative weightage DM provides has to
satisfy ∑ wn = 1 (Sum of all weights is unity).Therefore, w1 + w2 = 1
Using scalarization technique, the objective function is defined as the sum of all
the response characteristics. This is also called weighted sum method.
Using the equations from Regression of Experimental data,
f1(v,f,ap, rE) = [0.165 – (0.00017. cutting speed) + (12.69. feed rate)
+ (0.069. depth of cut) – (1.057. nose radius)]
f2(v,f,ap, rE) = [-27806 + (89.64 . cutting speed) + (111064 . feed rate)
+ (17908. depth of cut) + (109. nose radius)]
FINAL EQUATION READY FOR IMPLEMENTATION USING A
MATHEMATICAL TOOL:
Minimize z = w1. [0.165 – (0.00017. cutting speed) + (12.69. feed rate)
+ (0.069. depth of cut) – (1.057. nose radius)]
+w2. (- [-27806 + (89.64. cutting speed) + (111064.
feed rate) + (17908. depth of cut) + (109. nose radius)])
As desired, we accord a weight of 0.7 or 70% importance to SR (implies 0.3 or
30% importance to MRR). w1 is chosen as 0.7. Then, w2 = (1- 0.7) = 0.3
Owing to units of measurement, dimensions and physics of machining, etc…
numerically we observe that SR and MRR differ largely (Refer Table 2 Experiment
No. 21: compare an SR of 0.3707 µm and corresponding MRR of 7265.31 mm3
/ min).
Owing to this numerical difference in the values, changing weights does not show
larger variation on optimal input combination values (treatment).
9. Multi Objective Optimization of Cutting Parameters during Turning of EN31 Alloy Steel
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6. ALGORITHM
A Computer Scientist defines an algorithm: Given a problem, a computer scientist’s
goal is to develop an algorithm, a step-by-step list of instructions for solving any
instance of the problem that might arise. Implementing an algorithm gives solutions.
The Methodology used here is the Application of Ant Colony Optimization (a
type of Evolutionary Algorithm) to the Multi Objective Optimization of Cutting
Parameters. Evolutionary Algorithms as the name suggests are Algorithms that
draw from the concepts of Evolution and those that are readily found in nature.
The Algorithm used is Ant Colony Optimization (ACO) Algorithm.
ACO ALGORITHM
ACO Algorithm is a type of Swarm Interlligence.ACO Algorithm was proposed by
Marco Dorigo (1992). Ant Colony Optimization (ACO) is a met heuristic for solving
combinatorial optimization problems which belongs to swarm intelligence approaches
[10]. Artificial Ants are used in the Algorithm. An analogy with the way ant colonies
function has suggested the definition of a new computational paradigm, which is
called ant system (AS). The main characteristics of this model are positive feedback,
distributed computation, and the use of a constructive greedy heuristic. Real ants can
find a shortest path from a food source to the nest without using visual cues. They use
markers (chemicals called pheromones) and make use of smell concentration to
choose the path traversed the most. Pheromones are said to give “acquired search
experience”.
Pheromone is the key to group effectiveness and the means of communication
between the ants. Ants move randomly, but, when they encounter a pheromone trail,
they choose to either follow it or not follow it, and in case they follow it, they
reinforce the pheromone trail with their own pheromone. When constructing
solutions, at each step a candidate is chosen relatively to a transition probability which
depends on two factors:
Factor 1: a pheromone factor (τ) and Factor 2: a heuristic factor (η).
In ACO algorithms, a finite size colony of artificial ants with the characteristics
described above collectively searches for good quality solutions to the optimization
problem under consideration [9]. Ant Colony Optimization is readily suitable for
Figure 6 figure shows ants carrying
food
Figure. 7 An example of a real ant colony
(a) An ant follows BHD path by chance
(b) Both paths are followed with same probability
(c) Larger number of ants follow the shorter path
10. Raj Mohan B V S and Suresh R K
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problems where the path taken has a bearing on the objective function (minimum
distance etc… as used in Travelling Salesman Problem (TSP)). Implementation of the
Algorithm is carried out in Matlab.
ALGORITHM FOR ACO: <Step 1> Initialization of parameters <Step2>
Construction of solutions <Step 3> Pheromone updating rule <Step 4> Return
to Step 2 until a given stopping criterion satisfied.
7. DESIGN OF EXPERIMENTS (DOE)
Basically, DOE answers the question what experiments to conduct and in what order.
Statistical Design of Experiments refers to the process of planning the experiment so
that appropriate data will be collected and analyzed by statistical methods, resulting in
valid and objective conclusions. DOE helps us study effects of multiple variables
simultaneously and determine the factor levels combination for optimal result.
Table 1: Factors and their levels in the current study
The full factorial is not feasible (34
= 81 Experiments). Taguchi Design, a highly
fractional factorial DOE is utilized. The current experimentation has been done
according to Taguchi Orthogonal Array
L 27 (34
)
No of Experiments: 27
No of Factors: 04
No. of levels: 03
Four Three-Level factors have been studied. Design of Experiments is done using
statistical tool (Minitab). After preparing the Design, the coded values are replaced by
actual parameter values (for machine setting of controllable factors).
8. RESULTS AND DISCUSSION
Obtaining Useful and Optimal (spectrum of treatments) output of results for Multi
Objective Optimization from the Algorithm depend on 1. Knowledge and Experience
of DM and Programmer (Applicability to type of problem) 2. Effectiveness (Ability to
solve the complexity of problem and give Pareto Frontier with minimal runtime user-
changes of problem bounds and algorithm parameters) 3.Efficiency (computational,
resources, Quality Time and Cost). Experience is a hard teacher (test first, lesson
next).
General Methods to get initial solutions: Once the direct formulae(if any)/
regression equations of Response Characteristics and their validity for the applied
input parameter bounds is known, different methods can be applied to solve the
problem. Brute Force Method can be utilized to effortlessly test the Regression
Equations at every possible treatment (full factorial of 81 treatments) and achieve the
Factor Level 1 Level2 Level3
1 CuttingSpeed (m/ min) 100 150 200
2 Feed rate (mm/ rev) 0.10 0.12 0.15
3 Depth of cut (mm) 0.4 0.8 1.2
4 Insert Nose Radius (mm) 0.4 0.8 1.2
11. Multi Objective Optimization of Cutting Parameters during Turning of EN31 Alloy Steel
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theoretically best treatment. Such a Brute Force method readily gave {200, 0.15, 1.2,
1.2} as the optimal solution in the current study with the value of Minimization
Objective Function, z = -8520.60 units. Software like LINGO are readily available
for Non Linear Optimization problems and arrive at the optimal solution (based on the
software internal algorithm coding abilities) given the function(s) and bounds as
input. LINGO readily gave {200, 0.15, 1.2, 1.2} as the optimal solution. Genetic
Algorithms (using Matlab inbuilt function for Genetic Algorithms: ga) also gave
{200, 0.15, 1.2, 1.2} as the optimal solution. Ant Colony Optimization also gave
{200, 0.15, 1.2, 1.2} as the optimal solution which is understandable as the optimal
treatment(s) can be found by any Algorithm if the Algorithm naturally lends itself to
Application to the type of problem and it comes as no surprise that changing
Algorithms does not change the optimal treatment because in scalarization or
weighted sum method, the optimal treatment is very straightforward and mere change
of Algorithm may not result in a new and better optimal treatment (if such a treatment
does not exist/ cannot be found by the Algorithm).
Weighted Sum Approach is Straight Forward compared to finding all the
Pareto Optimal Solutions (Pareto Frontier): A mention of Pareto Optimality is
made purely owing to the significance of such a solution. In Multi objective
Optimization Programming, the desired goals are often conflicting against each other
and it is not possible to satisfy all the goals at a time. Hence it gives a set of non
inferior solutions also known as Pareto Optimal solutions. The Pareto Optimal
solution refers to a solution around which there is no way of improving any objective
without degrading atleast one other objective (Deb et al 2002). Unless of course
Pareto Optimality (Non dominated points) theory comes into picture, finding
optimality at one unique weight combination using weighted summation is pretty
much straight forward. Enlightening and useful work of researchers like V Pareto, H
A Taha, S S Rao, E Zitzler, Kalyanmoy Deb, Anas Alfaris and many others in this
domain is very inspiring to newcomers and thanks are expressed to them for their
articles sharing the knowledge.
RESULTS from the Algorithm: The proof of the pudding is in the eating.
The results are the best measure for the efficacy of the method.
Results in this study are of 2 types (T1). Results from Experimentation (SR
from Talysurf instrument) (T2). Results from Algorithms (SR based on Regression of
Expt. values and MRR based on Regression of MRR values obtained by formula).
While deciding the optimal treatment, not only the numerical values of projected
(theoretical algorithm result)/ actual (experiment result) Response characteristics are
to be kept in mind but also the feasibility. For example treatment of { v, f, ap, rE } =
{ 200, 0.15, 1.2, 0.4 } may look numerically optimal on seeing the respective
Response Characteristics values. But insert with nose radius of 0.4 mm cannot be
given a depth of cut of 1.2 mm (not practically feasible) and has to be rejected on the
sieve of practicality. The next optimal solution which is not only mathematically
optimal but also practically feasible is to be chosen be it DOE Result by
Experimentation or Theoretical results from Algorithms based on Regression/
Formulae for Response Characteristics.
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Table 3 Results from Algorithm vs Experimental Results
Objective Function
Min. z = w1 . ( SR ) + w2 . ( - MRR )
User chosen input parameter bounds
v ε [100, 200]
fn ε [0.1, 0.15]
ap ε [0.4, 1.2]
rE ε [0.4, 1.2]
Method of obtaining Results
Results from
Algorithm based
on Regression of
SR and MRR
Experimental
Results
from
L 27 ( 34
)
ACO Turning
P
A
R
A
M
E
T
E
R
S
I
N
P
U
T
Cutting Speed v (m/ min) 200 200
Feed Rate fn (mm/ rev) 0.15 0.12
Depth of Cut ap (mm) 1.2 1.2
Nose Radius of Insert rE (mm) 1.2 1.2
R
E
S
P
Surface Roughness SR (µm) 0.8489 0.4412
Material Removal Rate MRR (mm3
/
min)
28402.00 26537.09
O
B
J
F
N
z (Obj Fn)
Min. z = w1 . ( SR )
+ w2 . ( -MRR)
where: w1 = 0.7 and w2 = 0.3
-8520.60 -7960.81
Different inputs to the Algorithm (input bounds, Multi Objective Optimization
Equation containing the weighted summation of response characteristics, constraint
inequalities etc…) and treatment combinations generated by the Algorithm and
subsequent computations determine the Algorithm output quality. As the results from
the ACO Algorithm achieve the optimal treatment (factors and levels combination), it
can be inferred that the Algorithm meets the requirement of optimization.
9. CONCLUSION
The following conclusions can be drawn from the study
(C1) Results of Optimization and the optimal combination
To find the optimal combination was the original goal. Optimal Combination of the
Input Cutting Process parameters was finally arrived at.
Case (1) Multi Objective (SR and MRR)
At w1 = 0.7 and w2 = 0.3, Objective function z = -7960.81 units for
{v, f, ap, rE } = {200, 0.12, 1.2, 1.2}.
Case (2) Single Objective
Case (2.1) Single Objective (Only SR) Objective function z = SR = 0.3707 µm for {v,
f, ap, rE } = {200, 0.1, 0.4, 1.2}
14. Raj Mohan B V S and Suresh R K
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Case (2.2) Single Objective (Only MRR). Objective function z = MRR = 26537.09
mm3
/ min for {v, f, ap, rE } = {200, 0.12, 1.2, 1.2}. It is noteworthy that this treatment
is the same setting obtained for Multi Objective Optimization [Case (1) above] also
(owing to the large numerical value of MRR compared to smaller numerical value of
SR at each setting owing to units of measurement, dimensions, physics of machining
etc…, dominance of MRR is understandable in deciding overall optimality whether
the weight for MRR is set at 0.3 or 1.0)
(C2) Performance of the Algorithm
ACO has been used for Turning optimization. The Results from Algorithm (based on
Regression Equation of Surface Roughness of Experimental Results and Regression
Equation of MRR obtained by fitting MRR by formula) meet the experimental results.
Multi Objective (SR and MRR) (from ACO Algorithm). At w1 = 0.7 and w2 = 0.3,
Objective function z = -8520.60 units for {v, f, ap, rE } = {200, 0.15, 1.2, 1.2}.
Oblivious to some or Readily Evident to the awakened manufacturer with an eye for
detail is the silent law, ever-vigilant and actively functioning karmic law of
machining: As you sow (input cutting conditions), so you reap (response
characteristics).
(C3) Increased Efficiency owing to Optimization
A Conclusion can be drawn that obtaining and using Optimal Cutting Parameters
helps Process, Energy, Time and Cost Efficiencies. Optimization of Cutting
parameters leads to a scientific and quantifiable improvement in manufacturing
process which in turn leads to increased quality/ profit.
Scope for Further Work
Further useful work is possible in this area. It is felt that there is a scope to integrate
the Optimization of Machining Parameters into a feature rich and ubiquitous
Computer Aided Engineering (CAE)/ Product Lifecycle Management (PLM) software
in the future. Change being the only constant, changing for the better all the time, we
can move towards a new paradigm of Changing for End to End Effective and
Efficient Manufacturing (C4E2E_EEM).
10. BENEFITS OF THIS RESEARCH WORK
Savings in Cost and Increase in Productivity. 2. Today, Quality Responsive
Excellence is the most important business objective and Achieving Quality is a need
and this study helps achieve it. 3. End-User benefits from Optimization using EA
(ACO).
11. ACKNOWLEDGEMENTS
To my mother and grandmother for their kind love and caring help in my life. Thanks
to Lord Ganesha the remover of obstacles. Sri Gnana Prasunambika Sametha
Srikalahasteeswara Swami. To Lord Lakshmi Narasimha. To the God Absolute for his
grace on us. Lord Hanuman, the reservoir of energy. To various Gurus and Teachers
who showed the way.
15. Multi Objective Optimization of Cutting Parameters during Turning of EN31 Alloy Steel
using Ant Colony Optimization
http://www.iaeme.com/IJMET/index.asp 45 editor@iaeme.com
REFERENCES
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International & 26th All India
Manufacturing Technology, Design and Research Conference (AIMTDR 2014)
December 12th–14th , 2014, IIT Guwahati, Assam, India
[2] Abhang L B et al, Modeling and Analysis for Surface roughness in Machining
EN-31 steel using Response Surface Methodology, International Journal of
Applied Research in Mechanical Engineering, 1(1)2011
[3] Kalyanmoy Deb and Rituparna Datta, Hybrid Evolutionary Multi Objective
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Wear, Workpiece Surface Temperature and Material Removal Rate in Turning of
AISI D2 Steel, IJAME 4(3) 2014, pp.291-298
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![Raj Mohan B V S and Suresh R K
http://www.iaeme.com/IJMET/index.asp 32 editor@iaeme.com
1. INTRODUCTION
Today, Quality Responsive Excellence is the most important business objective
(compared to earlier philosophy of manufacturing high quality product at optimal cost
that justifies the price of the product). The purpose of undertaking Cutting Parameter
Optimization in Turning is two-fold: Quality and Economics.
Turning is a widely used Industrial Manufacturing process for circular cross-
section components. A CNC Turn Center or a Conventional Lathe (like Engine lathe)
is used depending on market (external customers), manufacturing demands, enterprise
skills and monetary resources. A Manufacturing process involves a number of process
parameters (controllable and uncontrollable) which affect the output (response
variable). Turning Cutting Parameters (input cutting parameters eg: cutting speed,
feed rate, depth of cut, coolant, material and geometry of insert used for cutting) have
a bearing on Quality, Time and Cost (QTC) of the resultant finished component (for a
given work material grade). First the objectives (Response) have to be written down,
followed by control factors and their bounds, practical constraints of machining.
Enquiring Experienced Industrial Engineers and Machine Operators/ using Machining
Data Handbooks/ trial machining have to be performed for suitable choice of cutting
parameters. Cutting speed is the major factor to be controlled during machining as it
not only determines the production rate but also the tool life. After finding limits of
cutting speed, applicable feeds must be calculated (feed depends on tool (insert) nose
radius). Depth of cut should be adjusted for good cut. Choose Feed & Depth of cut as
appropriate to the cutting material and set the parameters observing the resultant
chips. Cutting insert (material and geometry) should be chosen based on applicability
to work material (hardness and other criteria). An excessively large nose radius of
insert can lead to vibration tendencies. For roughing, use larger nose radius (example:
rE = 1.2 mm) and for finishing use nose radius of 0.8 mm or 1.2 mm.
The basic idea of Optimization is to increase quality and reduce cost and find the
optimal set of input parameters. Optimal Turning Cutting Parameters are usually
picked by an experienced human technician (Process Planner or Machine Operator)
driven by wisdom (success or failure in component machining), combination of
material and size of work, material and size of cutter, available range of machine
variables and machine accuracy limitations and data from standard Industrial
Handbooks. Reduced Manufacturing Time, Good Tool Life and Good Surface Finish
(in finishing operation) are also important considerations. Coolant has been used in
the current machining (i.e, wet turning) as coolant is a must from friction heat
removal and tool life criteria. Coolant reduces heat considerably and has added
benefits (like reduction of BUE- Built Up Edge). The Objective of this work is to
obtain the optimal combination of the selected controllable input cutting parameters
namely cutting speed, feed rate, depth of cut and nose radius of insert edge for the
Objective Function comprising of the weighted summation of chosen group of
response characteristics [Surface Roughness (SR) and Material Removal Rate
(MRR)]. As the number of objectives (response characteristics) is two, this is termed
a Multi Objective Optimization problem (as opposed to Single Objective
Optimization wherein there is only one objective). The target value Surface
Roughness is also a goal to achieve. The aim is to determine the BEST AND
PRACTICALLY FEASIBLE COMBINATION of {cutting speed, feed rate, depth
of cut, nose radius} which gives optimal {SR, MRR}. The Research Procedure
uses Ant Colony Optimization (ACO) to get the response characteristics. For MRR,
the Regression of values obtained by formula is used; and for SR, the Regression of](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)