Experimental Investigations on Optimization Of

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International Journal of Advanced Engineering Research and Studies E-ISSN22498974

IJAERS/Vol. I/ Issue III/April-June, 2012/55-64

Research Article

EXPERIMENTAL INVESTIGATIONS ON OPTIMIZATION OF

ULTRASONIC WELDING PARAMETERS FOR COPPER TO

BRASS JOINTS USING RESPONSE SURFACE METHOD AND

GENETIC ALGORITHMS Elangovan

1*, S Venkateshwaran

2, K Prakasan

3Address for Correspondence

1Associate Professor,

2Under Graduate Student,

3Professor

Department of Production Engineering, P.S.G College of Technology, Coimbatore India 641004ABSTRACTIn this paper an effective methodology is developed to determine the optimum welding conditions that maximize the strengthof joints produced by ultrasonic welding by coupling response surface method (RSM) with genetic algorithm (GA). RSM is

utilized to develop an effective model to predict weld strength by incorporating process parameters such as pressure, weldtime and amplitude. Experiments were conducted as per central composite face centered design for spot and seam welding of

0.2 and 0.3 mm thick copper and brass specimens. An effective second order response surface model is developed byutilizing experimental measurements. Response surface model is further interfaced with the GA to optimize the welding

conditions for desired weld strength. Optimum welding conditions produced from GA is verified with the experimentalresults and is found to be in good agreement.

KEYWORDS Optimization, Response surface method, Genetic algorithm, Ultrasonic metal welding, Weld strength.

1. INTRODUCTION

Copper and brass alloys are extensively used in

automobile industries, heat exchanger and electricalapplications owing to its high thermal conductivity,

strength and retention of strength at sufficiently

elevated temperatures. The conventional welding

process of copper and brass produces large heat

affected zone (HAZ) and fusion zone (FZ), high

shrinkage, variations in microstructures and

properties, evaporative loss of alloying elements,

high residual stress and distortion which calls for the

development of a solid-state joining process in which

metallurgical bonding between similar or dissimilar

materials can be created without melting. One suchsolid-state joining process is ultrasonic metal welding

(USMW).

Figure 1 Schematic representation of ultrasonic

metal weldingUSMW is a process in which similar or dissimilar

metallic components are joined by the application of

high frequency vibrations which are in plane with the

interface under moderate pressure as shown in Figure

1. The high frequency relative motion between the

parts leads to solid progressive shearing and plastic

deformation which causes a localized joining in few

seconds without producing significant amount of heatand without causing changes in the properties of

work pieces. In USMW at least one part must be

relatively light, as it would take tremendous amount

of energy to vibrate a heavy part at the necessary

frequency which limits the applicability of the

process to small components and wires.

The process modeling by RSM using statisticaldesign of experiments based on central composite

face centered design is proved to be an efficient

modeling tool. This method not only reduces the cost

and time but also gives the required information

about the main and interaction effects. In this study, a

second order response surface (RS) model for

predicting weld strength of ultrasonically welded

copper to brass specimens is developed. The

accuracy of the RS model is verified with the

experimental studies. The developed RS model is

further coupled with genetic algorithm (GA) to findthe optimum welding conditions leading to the

maximum weld strength. The predicted optimumwelding condition by GA is validated with

experimental results.

The use of genetic algorithm (GA) as a tool for

process optimization is rapidly becoming anestablished approach. The GA combines the

Darwinian principle of natural selection survival of

the fittest strategy to eliminate unfit solutions and

use random information exchange, with an

exploitation of knowledgecontained in old solutions,

to result in a search mechanism with surprisingpower and speed. GA using gene information and

chromosome processing to optimize the given

function, proved to be an efficientoptimization tool[1]. The field of ultrasonic metal welding is one of

the important topics in the manufacturing of

accessories used in automotive, heat exchanger andelectrical applications. Many researchers have

reported their research work pertaining to the

mechanism of joint formation, temperature

distribution at the weld interface and joint strength,

etc,. Some of the important observations arepresented below.

Gaitonde et al. [1] developed the second order

mathematical models for minimization of burr height

and burr thickness using RSM. In this study, five

level half replicate second order rotatable centralcomposite designs was adopted to study the effect ofinteractions. The developed RSM models were used

as a fitness function in GA to optimize the process

parameters ford drilling. The developed model RSM


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model was tested through ANOVA and was found to

be adequate.

Padmanaban and Balasubramanian [2] developed an

empirical relationship using RSM to predict tensile

strength of laser beam welded AZ31B magnesium

alloy. The authors have used three factor, three level

central composite face centered design to optimize

the parameters. They identified that the welding

speed has the greatest influence on tensile strength,

followed by laser power and focal position.Research by Nuran Bradley [3] emphasized on

design, modeling and analysis of RSM and explained

the first-order, the second-order, and three- level

fractional factorial in depth. The author explained the

advantages and limitations of each models

numerically and graphically.

Kumar et al. [4] proposed a methodology to improve

the mechanical properties of AA 5456 aluminum

alloy welds in magnetic arc oscillation welding

process. The authors have used Taguchis method to

optimize the process parameters. The percentage of

error between experimental and predicted values was

found to be very small. Microstructures of all thewelds were studied and correlated with the

mechanical properties.

Research by De Vries [5] discussed the mechanics

and mechanism of USMW. Temperature was

measured for the welding of aluminum by infrared

camera for different welding conditions. It was found

that interface temperature varied from 40 to 80

percentage of the melting point depending on thevalue of the parameters used for welding.

Watanabe et al.[6] investigated the effect of welding

conditions on the mechanical properties and the

interface microstructure of the welded joint while

joining mild steel sheet to aluminum alloy sheetcontaining magnesium. From the experimental resultsit is observed that weld strength decreases with

increasing of clamping force, because the excessive

clamping force reduced the frictional action at the

interface.

Meran [7] developed the Genetic Algorithm Welding

Current/Velocity Estimation Models(GAWCEM/GAWVEM) to optimize the parameters

like weld current and weld velocity in tungsten inert

gas (TIG) welding. The developed models are

compared with experimental data and are found to be

in good agreement.

Onwubolu and Shivendra Kumar [8] presented amathematical model for correlating the interaction of

drilling parameters and their effect on the cutting tool

using RSM in CNC drilling process. In this work,

three level full factorial designs were chosen for

experiments. The optimam combinations of theseparameters from RSM were useful for minimizing the

axial force and torque eduring drilling operations.

Elangovan et al. [9] made a systematic study on

ultrasonic welding of copper to optimize of the

process parameters using Taguchi method. L27

Orthogonal array was chosen for this study byconsidering the control factors and their interactions.

Through ANOVA it was shown that pressure,amplitude and time are the important welding

parameters that influence weld strength.

Canyurt et al. [10] developed the genetic algorithm

weld strength estimation model (GAWSEM) to

estimate the weld strength of brass using hybrid laser

welding. The estimated results indicated that

GAWSEM model can be used as an estimation

technique to predict the weld parameters which give

the quality welds for brass material.

Habib [11] discussed the development of a

comprehensive mathematical for correlating the

interactive and higher order influences of various

parameters in electrical discharge machining through

RSM utilizing relevant experimental data. Theadequacy of the above proposed models has been

tested through ANOVA.

Thus, from the literature review it is observed that

weld pressure, amplitude and weld time are critical

parameters in deciding the weld strength and quality

of the weld. Many researchers have developed

second order mathematical model using RSM for

different processes like drilling, Tungsten Inert Gas

(TIG) welding, laser hybrid welding and electric

discharge machining. Then the mathematical model

is used in genetic algorithm as a fitness function to

optimize the process parameters. It seems that no

work has been reported in ultrasonic welding ofcopper - brass wherein welding parameters for

maximizing weld strength using RSM and GA is

considered. So optimization of parameters while

joining copper brass specimens using USMW by

RSM and GA has been attempted in this work.

2. EXPERIMENTAL PROCEDURES

2.1 Plan of Experiments

An important stage in response surface modelgeneration by RSM is the planning of experiments.

From the literature survey, factors which have a

significant influence on weld strength of ultrasonic

metal welding were identified. They are weld

pressure, weld time and amplitude of vibration ofhorn.Large numbers of trial runs were carried out using

0.2 and 0.3 mm thick copper-brass specimens to

determine maximum and minimum values of

ultrasonic welding parameters. In this study,

experiments are planned as per Central Composite

Face Centered (CCF) design with the star points atthe center of each face of factorial space was used for

spot and seam welding of 0.2 and 0.3 mm thick

copper brass joints. This design fits the second

order response surface very accurately [2]. From the

trial runs the most suitable parameters were identified

which is listed in Table 1.Table 1 Range of variables for joining of Cu -

brass specimens

2.2 Experimental details

The experimental setup for the USMW is shown in

Figure 2 with data acquisition system (DAQ).

Welding was carried out using a conventionalultrasonic metal welding machine (2500 W, 20 kHz)

for different ranges of weld parameters. Experimentsare carried out using the design matrix as developed

in Table 1. In this work horn made of hardened steel

with diamond knurl pattern (seam and spot) and anvil

Factor Notation Unit Factor Level

-1 0 +1

Pressure (x1) p bar 3.0 3.5 4.0

Weld time(x2) t sec 2.5 3.0 3.5

Amplitude(x3) a m 28 42.5 57


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made of steel with serrations on top surface were

used. The horn is serrated near the tip for preventing

the workpiece from sliding during welding. The

specimens (0.2mm and 0.3mm thick pure copper and

brass) were prepared according to ASTM standard (D

1002 01) [12] for testing strength of the joint by

tensile loading. Before welding, samples were

cleaned with acetone to remove the surface impurities

as it may affect the bond strength. Figure 3 shows the

standard size of specimen as per ASTM standard.

Figures 4 and 5 show the actual spot and seam

welded samples of copper - brass work pieces. A

computerized tensile testing machine was used to

determine the weld strengths. During the tensile

testing, ductile fracture was observed at weld

interface for most of the welded samples and some of

the fractured samples were shown in figure 6.

Figure 2 Experimental set up for ultrasonic metal welding

Figure 3 ASTM standard (D 1002 01)for weld specimen

Figure 4 Spot welded specimens of Cu-brass (0.3 mm


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Figure 5 Seam welded specimens of Cu-brass (0.2 mm thick)

Figure 6 Spot welded specimens after tensile test (0.2 mm thick)

3.3 Response surface model for weld strength

The Response Surface Methodology (RSM) is a

collection of mathematical and statistical techniquesuseful for the modeling and analysis of problems in

which a response of interest isinfluenced by several

variables and the objective is to optimize thisresponse [13]. The second order mathematical

models have been developed to predict the weld

strength. The polynomial equation for three factors

considered in the present case is

= =

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