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CERTIFICATE

This is to certify that the project entitled” METALLURGICAL INVESTIGATION OF

MICROSTRUCTURE OF THE WELD BEAD IN SAW WELDING USING THE RECYCLED

FLUX WITH THE HELP OF “MATLAB SOFTWARE”.

Being submitted by:

RAHUL VIKRAM (04231169) GWT/7709/04

DEEPAK KUMAR CHOUDHARY (04231151) GWT/7703/04

SANDEEP BANSAL (04231172) GWT/7786/04

To Sant Longowal Institute of Engineering & Technology towards the partial fulfillment

Of the requirements for the award of B.Tech degree is the record of bonafide work

carried out by students under my supervision and guidance.

(Signature)

Mr. KULWANT SINGH (Assistance Professor Mech. Engg. Deptt.)

SR. NO

CONTENTS

PAGE NO.

1

Acknowledgement

i

2

Abstract

ii

3

List of figures

iii

4

INTRODUCTION TO MATLAB 4.1 What is MATLAB? 4.2 the MATLAB system 4.3 MATLAB desktop 4.4 desktop tools

1 2 3 4 5

5

MATLAB TOOLBOXES 5.1 toolboxes description

11 12

6

IMAGEPROCESSING TOOLBOX 6.1 Image processing 6.2 Image processing toolbox 6.3 Key features 6.4 Importing and exporting images 6.5 Pre and post processing images 6.6 Enhancing images 6.7 Images processing toolbox demos 6.8 Thresholding

14 15 15 15 16 16 16 17 18

7

METALLURGICAL INVESTIGATION 7.1 Grain size determination 7.2 Specimen preparation for optical microscopy 7.3 Microstructures 7.4 Properties of weld zone of weld bead (B3, B4, B5, B6) 7.5 Properties of base metal of weld bead of weld bead (B3, B4, B5, B6) 7.6 Properties of heat affected zone of weld bead (B3, B4, B5, B6) 7.7 Work piece samples 7.8 Calculation for finding heat input for different weld bead

19 22 23 24 33 37 37 38 40

8

MATLAB PROGRAMME 8.1 color image programme 8.2 image value programme

42 43 44

9

RESULT & DISCUSSION 9.1 B3. weld bead results 9.2 B4. weld bead results 9.3 B5. weld bead results 9.4 B6. weld bead results

46 47 49 51 53

11

CONCLUSION

55

12 REFERENCE 58

ACKNOWLEDGEMENT

We deem it as a proud privilege to express our sincere regard and

gratitude to Mr. KULWANT SINGH (Assistance Professor Mech. Engg. Deptt.),

who as a project guide provided this valuable opportunity to pursue this work with him,

His sincere support and continuous guidance helped us in overcoming all the hurdles

that came during the progress of this project.

We hereby express our deep gratitude to DR. ANAND J. VAZ HOD

(Mechanical Engg.) for extending a hand of support whenever needed in the practical

work.

We would like to show our deep regards to SRI. A.S. SHAHI Project co-

coordinator, Deptt. Of Mech. Engg. SLIET and his valuable assistance regarding project

submission.

And last but not least, we are grateful our worthy Director, for her deep concern

about formulating the syllabus of degree module.

RAHUL VIKRAM

DEEPAK KUMAR CHOUDHARY

SANDEEP BANSAL

ABSTRACT

The metallurgical and mechanical properties of weld metal depend upon its

microstructure which is governed by the rate of heat input. The rate of heat input is

further dependant on the welding parameters. Weld metal generally constitutes of grain

boundary ferrite, polygonal ferrite and acicular ferrite. In order to have maximum

toughness and good strength it is desirable to obtain maximum amount of acicular ferrite

in the weld metal.

So, investigations were carried out to study the microstructure of weld metal.

Beads on plates were deposited at various heat input by changing the welding parameters

accordingly. The microstructure of the base metal, heat affected zone and weld metal was

investigated with metallurgical microscope. The microstructures were examined and

analyses were done. It was found out that the weld metal which was welded with highest

heat input has maximum concentration of acicular ferrite in its weld zone. As we know

that the weld metal that contains maximum concentration of acicular ferrite has better

mechanical properties and thus this type of a microstructure is desirable.

List of Figures

Fig No. Explanation

1 MATLAB desktop

2 Command window

3 Command history

4 Launch pad

5 Help browser

6 Current directory browser

7 Workspace browser

8 Array editor

9 Editor/ debugger

10 Optical Microscope Fitted with 35mm Camera and Digital Camera

11 SAMPLE NO: B3

12 SAMPLE NO: B4

13 SAMPLE NO: B5

14 SAMPLE NO: B6

15 WELD ZONE OF SAMPLE B3


17 WELD BEAD ZONE OF SAMPLE B5


19 The welded metal specimens over which the microstructures are being

analyzed.

20 Microstructure of B3.weld bead before image processing

21 Microstructure of B3.weld bead after image processing

22 Percentage distribution of phases in microstructure of B3. weld bead




Fig No. Explanation







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DEPARTMENT OF MECHANICAL ENGINEERING 1

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What Is MATLAB?

MATLAB is a high-performance language for technical computing. It integrates computation, visualization,

and programming in an easy-to-use environment where problems and solutions are expressed in familiar

mathematical notation. Typical uses include:

• Math and computation

• Algorithm development

• Modeling, simulation, and prototyping

• Data analysis, exploration, and visualization

• Scientific and engineering graphics

• Application development, including graphical user interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning.

This allows you to solve many technical computing problems, especially those with matrix and vector

formulations, in a fraction of the time it would take to write a program in a scalar no interactive language

such as C or FORTRAN.

The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy

access to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB uses

software developed by the LAPACK and ARPACK projects, which together represent the state-of-the-art

in software for matrix computation.

MATLAB has evolved over a period of years with input from many users. In university environments, it is

the standard instructional tool for introductory and advanced courses in mathematics, engineering, and

science. In industry, MATLAB is the tool of choice for high-productivity research, development, and

analysis.

MATLAB features a family of application-specific solutions called toolboxes. Very important to most users

of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive

collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular

classes of problems. Areas in which toolboxes are available include signal processing, control systems,

neural networks, fuzzy logic, wavelets,

simulation, and many others.

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The MATLAB System:- The MATLAB system consists of five main parts: -

Development Environment. This is the set of tools and facilities that help you use MATLAB functions and

files. Many of these tools are graphical user interfaces. It includes the MATLAB desktop and Command

Window, a command history, and browsers for viewing help, the workspace, files, and the search path.

The MATLAB Mathematical Function Library. This is a vast collection of computational algorithms

ranging from elementary functions like sum, sine, cosine, and complex arithmetic, to more sophisticated

functions like matrix inverse, matrix eigen values, Bessel functions, and fast Fourier transforms.

The MATLAB Language. This is a high-level matrix/array language with control flow statements,

functions, data structures, input/output, and object-oriented programming features. It allows both

“programming in the small” to rapidly create quick and dirty throw-away programs, and “programming in

the large” to create complete large and complex application programs.

Handle Graphics®. This is the MATLAB graphics system. It includes high-level commands for two-

dimensional and three-dimensional data visualization, image processing, animation, and presentation

graphics. It also includes low-level commands that allow you to fully customize the appearance of graphics

as well as to build complete graphical user interfaces on your MATLAB applications.

The MATLAB Application Program Interface (API). This is a library that allows you to write C and

FORTRAN programs that interact with MATLAB. It include facilities for calling routines from MATLAB

(dynamic linking), calling MATLAB as a computational engine, and for reading and writing MAT-files.

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MATLAB Desktop: - When we start MATLAB, the MATLAB desktop appears, containing tools

(graphical user interfaces) for managing files, variables, and applications associated with MATLAB. The

first time MATLAB starts, the desktop appears as shown in the following Illustration, although Launch Pad

may contain different entries.

Figure1. MATLAB desktop

We can change the way our desktop looks by opening, closing, moving, and resizing the tools in it. We can

also move tools outside of the desktop or return them back inside the desktop (docking). All the desktop

tools provide common features such as context menus and keyboard shortcuts. We can specify certain

characteristics for the desktop tools by selecting Preferences from the File menu. For example, we can

specify the font characteristics for Command Window text. For more information, click the Help button in

the Preferences dialog box.

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Desktop Tools: - This section provides an introduction to MATLAB’s desktop tools. You can also

use MATLAB functions to perform most of the features found in the desktop tools. The tools are: -

•“Command Window”

•“Command History”

•“Launch Pad”

•“Help Browser”

•“Current Directory Browser”

•“Workspace Browser”

•“Array Editor”

•“Editor/Debugger”

Command Window: - Command Window is use to enter variables and run functions and M-files.

Figure2. Command window

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Command History: - Lines we enter in the Command Window are logged in the Command History

window. In the Command History, we can view previously used functions, and copy and execute selected

lines.

Figure3. Command history

Launch Pad: - MATLAB’s Launch Pad provides easy access to tools, demos, and documentation.

Figure4. Launch pad

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Help Browser: - Use the Help browser to search and view documentation for all our MathWorks

products. The Help browser is a Web browser integrated into the MATLAB desktop that displays HTML

documents. To open the Help browser, click the help button in the toolbar, or type helpbrowser in the

Command Window.

Figure5. Help browser

Current Directory Browser: - MATLAB file operations use the current directory and the search

path as reference points. Any file you want to run must either be in the current directory or on the search

path. A quick way to view or change the current directory is by using the Current Directory field in the

desktop toolbar as shown below.

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To search for, view, open, and make changes to MATLAB-related directories and files, use the MATLAB

Current Directory browser. Alternatively, you can use the functions dir, cd, and delete.

Figure6. Current directory browser

Workspace Browser: - The MATLAB workspace consists of the set of variables (named arrays)

built up during a MATLAB session and stored in memory. We add variables to the workspace by using

functions, running M-files, and loading saved workspaces. To view the workspace and information about

each variable, use the Workspace browser, or use the functions who and whos. To delete variables from the

workspace, select the variable and select Delete from the Edit menu. Alternatively, use the clear function.

The workspace is not maintained after you end the MATLAB session. To save the workspace to a file that

can be read during a later MATLAB session, select Save Workspace As from the File menu, or use the

save function. This saves the workspace to a binary file called a MAT-file, which has a .mat extension.

There are options for saving to different formats. To read in a MAT-file, select

Import Data from the File menu, or use the load function.

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Figure7. Workspace browser

Array Editor: - Double-click on a variable in the Workspace browser to see it in the Array Editor. Use the Array Editor to view and edit a visual representation of one- or two-dimensional numeric arrays, strings, and cell arrays of strings that are in the workspace.

Figure8. Array editor

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Editor/Debugger: - Use the Editor/Debugger to create and debug M-files, which are programs you write to run MATLAB functions. The Editor/Debugger provides a graphical user interface for basic text editing, as well as for M-file debugging.

Figure9. Editor/Debugger

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MATLAB TOOLBOXES: -

Toolboxes Description:-

• Bioinformatics: - Read, analyze, and visualize genomic, proteomic, and microarray data in

MATLAB.

• Communications: - Design and analyze algorithms for the physical layer of communication

systems.

• Control System: - Design and analyze controllers for dynamic closed-loop systems.

• Curve Fitting: - Perform model fitting and analysis.

• Data Acquisition: - Acquire and send out data from plug-in data acquisition boards.

• Database: - Exchange data with relational databases.

• Filter Design: - Design and analyze fixed-point, adaptive, and multirate filters.

• Filter Design HDL Coder: - Generate VHDL and Verilog code for fixed-point filters from

MATLAB.

• Financial: - Analyze financial data and develop financial algorithms.

• Fixed-Point: - Design and verify fixed-point algorithms and analyze fixed-point data.

• Fuzzy Logic: - Design and simulate fuzzy logic systems.

• Genetic Algorithm and Direct Search: - Solve optimization problems using genetic or direct

search algorithms.

• Image Acquisition: - Acquire images and video from industry-standard hardware.

• Image Processing: - Perform image processing, analysis, and algorithm development.

• Instrument Control: - Control and communicate with test and measurement instruments.

• LMI Control: - Design robust controllers using convex optimization techniques.

• Link for Code Composer Studio: - Use MATLAB with RTDX-enabled Texas Instruments digital

signal processors.

• Link for ModelSim: - Cosimulate and verify VHDL and Verilog using ModelSim.

• Mapping: - Analyze and visualize geographically based information.

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• Model Predictive Control: - Control large, multivariable processes in the presence of constraints.

• Mu-Analysis and Synthesis: - Design multivariable feedback controllers for systems with model

uncertainty.

• Neural Network: - Design and simulate neural networks.

• OPC: - Read, write, and log data from OPC servers.

• Optimization: - Solve standard and large-scale optimization problems.

• Partial Differential Equation: - Solve and analyze partial differential equations.

• RF: - Design and analyze networks of RF components.

• Robust Control: - Design robust multivariable feedback control systems.

• Signal Processing: - Perform signal processing, analysis, and algorithm development.

• Spline: - Create and manipulate spline approximation models of data.

• Statistics: - Apply statistical algorithms and probability models.

• Symbolic Math: - Perform computations using symbolic mathematics and variable-precision

arithmetic.

• System Identification: - Create linear dynamic models from measured input-output data.

• Virtual Reality: - Animate and visualize Simulink systems in three dimensions.

• Wavelet: - Analyze and synthesize signals and images using wavelet techniques.

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IMAGE PROCESSING: -

Image processing is any form of information processing for which the input is an image, such as

photographs or frames of video; the output is not necessarily an image, but can be for instance a set of

features of the image. Most image-processing techniques involve treating the image as a two-dimensional

signal and applying standard signal-processing techniques to it.

The Image Processing Toolbox: - The Image Processing Toolbox provides a comprehensive set

of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and

algorithm development. We can restore noisy or degraded images, enhance images for improved

intelligibility, extract features, analyze shapes and textures, and register two images. Most toolbox functions

are written in the open MATLAB language. This means that we can inspect the algorithms, modify the

source code, and create our own custom functions.

The Image Processing Toolbox supports engineers and scientists in areas such as

biometrics, remote sensing, surveillance, gene expression, microscopy, semiconductor testing, image sensor

design, color science, and materials science. It also facilitates the learning and teaching of image processing

techniques.

Key Features

1. Image enhancement, including linear and nonlinear filtering, filter design, deblurring, and automatic

contrast enhancement.

2. Image analysis, including texture analysis, line detection, morphology, edge detection, segmentation,

region-of-interest (ROI) processing, and feature measurement.

3. Color image processing, including color space conversions and device-independent ICC profile

import and export.

4. Spatial transformations and image registration, including a graphical tool for control-point selection.

5. Interactive image display and modular tools for building image GUIs.

6. Support for multidimensional image processing.

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Importing and Exporting Images: - The Image Processing Toolbox supports images generated by a wide range of devices, including digital cameras, frame grabbers, satellite and airborne sensors, medical imaging devices, microscopes, telescopes, and other scientific instruments. You can visualize, analyze, and process these images in many data types, including single- and double-precision floating-point and signed or unsigned 8-, 16-, and 32-bit integers. There are several ways to import or export images into and out of the MATLAB environment for processing. You can use the Image Acquisition Toolbox (available separately) to acquire live images from Web cameras, frame grabbers, DCAM-compatible cameras, and other devices. Using the Database Toolbox (also available separately), you can access images stored in ODBC/JDBC-compliant databases.

Pre- and Post-Processing Images

The Image Processing Toolbox provides reference-standard algorithms for pre- and post-processing tasks that solve frequent system problems, such as interfering noise, low dynamic range, out-of-focus optics, and the difference in color representation between input and output devices.

Enhancing Images

Image enhancement techniques in the Image Processing Toolbox enable you to increase the signal-to-noise ratio and accentuate image features by modifying the colors or intensities of an image. You can:

• Perform histogram equalization • Perform decorrelation stretching • Remap the dynamic range • Adjust the gamma value • Perform linear, median, or adaptive filtering

The toolbox includes specialized filtering routines and a generalized multidimensional filtering function

that handles integer image types, multiple boundary padding options, and convolution and correlation.

Predefined filters and functions for designing and implementing your own linear filters are also provided.

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Image Processing Toolbox Demos: -

1. Color Segmentation

1. Color-Based Segmentation Using the L*a*b* Color Space

2. Color-Based Segmentation Using K-Means Clustering

2. Deblurring

1. Deblurring Images Using the Blind Deconvolution Algorithm

2. Deblurring Images Using the Lucy-Richardson Algorithm

3. Deblurring Images Using a Regularized Filter

4. Deblurring Images Using the Wiener Filter

3. Enhancement

1. Contrast Enhancement Techniques

2. Correcting Nonuniform Illumination

3. Enhancing Multispectral Color Composite Images

4. Intensity Adjustment and Histogram Equalization

5. Landsat Color Composite

6. Noise Reduction Filtering

7. Region-of-Interest Processing

4. Image Analysis

1. Edge Detection

2. Quadtree Decomposition

5. Image Arithmetic

1. Alpha Blending Streamed Image Pairs

6. Image Registration

1. Finding the Rotation and Scale of a Distorted Image

2. Registering an Aerial Photo to an Orthophoto

3. Registering an Image Using Normalized Cross-Correlation

7. Image Transformation

1. Creating a Gallery of Transformed Images

2. Exploring a Conformal Mapping

3. Extracting Slices from a 3-Dimensional MRI Data Set

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4. Padding and Shearing an Image Simultaneously

8. Measuring Image Features

1. Finding the Length of a Pendulum in Motion

2. Granulometry of Snowflakes

3. Identifying Round Objects

4. Measuring Angle of Intersection

5. Measuring the Radius of a Roll of Tape

9. Morphological Segmentation

1. Detecting a Cell Using Image Segmentation

2. Marker-Controlled Watershed Segmentation

10. Transforms

1. Discrete Cosine Transform

2. 2-D Filtering and Filter Design

3 .Reconstructing an Image from Projection Data

Thresholding:-

Thresholding is the simplest method of image segmentation. Individual pixels in a grayscale image are

marked as “object” pixels if their value is greater than some threshold value (assuming an object to be

brighter than the background) and as “background” pixels otherwise. Typically, an object pixel is given a

value of “1” while a background pixel is given a value of “0.”

The key parameter in thresholding is obviously the choice of the threshold. Several different methods for

choosing a threshold exist. The simplest method would be to choose the mean or median value, the rationale

being that if the object pixels are brighter than the background, they should also be brighter than the

average. In a noiseless image with uniform background and object values, the mean or median will work

beautifully as the threshold, however generally speaking, this will not be the case. A more sophisticated

approach might be to create a histogram of the image pixel intensities and use the valley point as the

threshold.

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METALLURGICAL INVESTIGATION

Metallography is the study of metals by optical and electron microscopes. Structures which are

coarse enough to be discernible by the naked eye or under low magnifications are termed macrostructures.

Useful information can often be gained by examination with the naked eye of the surface of metal objects or

polished and etched sections. Those which require high magnification to be visible are termed

microstructures. Microscopes are required for the examination of the microstructure of the metals. Optical

microscopes are used for resolutions down to roughly the wavelength of light (about half a micron) and

electron microscope are used for detail below this level, down to atomic resolution. The most commonly

used microscope is the conventional light microscope. In principle, optical microscopes may be used to look

through specimens (‘in transmission’) as well as at them (‘in reflection’). Many materials, however, do not

transmit light and so we are restricted to looking at the surface of the specimens with an optical microscope.

Electron microscope can be used in the transmission e.g. Transmission Electron Microscope (TEM) and to

look at the surfaces e.g. Scanning Electron Microscope (SEM) Microscopy can give information concerning

a material’s composition, previous treatment and properties. Particular features of interest are

(I) Grain size

(II) Phases present

(III) Chemical homogeneity

(IV) Distribution of phases

(V) Elongated structures formed by plastic deformation

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Optical Microscopy With optical microscopy, the light microscope is used to study the microstructure; optical

illumination systems are its basic elements. For materials that are opaque to visible light (all metals, many

ceramics and polymers), only the surface is subject to observation, and the light microscope must be used in

a reflective mode. Contrasts in the image produced result from differences in reflectivity of the various

regions of the microstructure. Careful and meticulous surface preparations are necessary to reveal the

important details of the microstructure. The specimen surface must first be ground and polished to a smooth

and mirror like finish. This is accomplished by using successively finer abrasive papers and powders. The

microstructure is revealed by a surface treatment using an appropriate chemical reagent in a procedure

termed etching. The etching reagents depend on the material used and after etching the specimen must be

washed with alcohol and ether to remove the grease. The atoms at the grain boundaries are chemically more

active, and consequently dissolve more readily than those within the grains forming small grooves. These

grooves become discernible when viewed under a microscope because they reflect light at an angle different

from that of the grains themselves.

When the microstructure of a two phase alloy is to be examined, an enchant is chosen that produces

a different texture for each phase so that the different phases may be distinguished from each other. The

maximum possible magnification with an electron microscope is approximately 2000 diameters.

Electron Microscopy:

Transmission Electron Microscopy (TEM):

The image seen with a TEM is formed by an electron beam that passes through the specimen. Details

of the internal micro structural features are accessible to observation; contrasts in the image are produced by

differences in beam scattering or diffraction produced between various elements of the microstructure or

defect. In the TEM, electrons are focused on an extremely thin foil of the material; the beam of electrons

interacts with imperfections in the material, causing differences in the fraction of electrons that are

transmitted. The transmitted beam is projected onto a fluorescent screen or a photographic film so that the

image may be viewed. Magnifications approaching 1000000x are possible with TEM. The TEM is used to

observe dislocations.

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Scanning Electron Microscopy (SEM):

The surface to be examined is scanned with an electron beam, and the reflected beam of electrons is

collected, then displayed at the same scanning rate on a cathode ray tube. The image that appears on the

screen, which may be photographed, represents the surface features of the specimen. The surface may or

may not be polished and etched, but it must be electrically conductive; a very thin metallic coating must be

applied to non conductive materials. Magnifications ranging from 10 to in excess of 50 000 diameters and

also very great depths of field are possible.

GRAIN SIZE DETERMINATION:

The grain size of metals is usually expressed as the American Society for Testing and Materials

(ASTM) grain size number. The ASTM has prepared 10 standards comparison charts, all having different

average grain sizes. To each is assigned a grain size number, n, ranging from 1to 10, the larger the number,

the smaller the grains. The designation is based on the equation, N = 2n-1

where, N is the number of grains in an area of 1 sq. in at 100x magnification.

A specimen must be properly prepared to reveal the grain structure, which is photographed at a

magnification of 100x. Thus a steel with N=6 has, on average, 32 grains in an area of 1 sq. in. at 100x. Grain

size is expressed as the grain size number of the chart that most nearly matches the grains in the micrograph.

Grain size may also be determined using an intercept method described below. Straight lines all of the same

length are drawn through several photomicrographs that show the grain structure. The grains intersected by

each line segment are counted; the line length is then divided by an average of the number of grains

intersected, taken over all the line segments. The average grain diameter is found by dividing this result by

the linear magnification of the micrographs.

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SPECIMEN PREPARATION FOR OPTICAL MICROSCOPY

The examination of materials by optical microscopy is essential in order to understand the

relationship between properties and microstructure. Metallography is the study of metals by optical

examination. This is most commonly done using a conventional light microscope. However useful

information can be gained by examination with the naked eye of the surface of metal objects or of polished

and etched sections. Structures which are coarse enough to be discernible be the naked eye is termed

macrostructures. Those which require magnification to be visible are termed microstructures.

Figure 10: Optical Microscope Fitted with 35mm Camera and Digital Camera

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MICROSTRUCTURES

• The preparation of a specimen to reveal its microstructure involves.

• Sawing the section to be examined

• Mounting in resins (if sample is too small)

• Coarse grinding

• Grinding on progressively finer emery paper

• Polishing using alumina powder or diamond paste on rotating wheel

• Etching in dilute acid (2% Nital for steel)

• Washing in Alcohol and drying

• Typical magnifications used are between 50x and 1000x

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Figure 11. SAMPLE NO: B3

(A). WELD AND BASE METAL

(B). BASE METAL

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(C). WELD ZONE

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(B). BASE METAL

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(C). WELD ZONE

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(B). BASE METAL

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(C). WELD ZONE

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(B). BASE METAL

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(C). WELD ZONE

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PROPERTIES OF WELD ZONE OF WELD BEAD (B3, B4, B5, B6) 1. PROPERTIES OF WELD ZONE OF WELD BEAD (B3)

A) Grain size is coarse B) Width of columns increased due to less heat input C) Grain boundary ferrite increased due to less heat input D) Acicular ferrite decreased due to less heat input E) Polygonal ferrite more F) Cast structure of weld zone

Figure 15. WELD ZONE OF SAMPLE B3

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2. PROPERTIES OF WELD ZONE OF WELD BEAD (B4)

A) Grain size is smaller than the B3 (WELD BEAD)

B) Width of columns smaller than the B3 (WELD BEAD) C) Grain boundary ferrite something less than the B3 (WELD BEAD) D) Polygonal ferrite less than the B3 (WELD BEAD) E) Cast structure of weld zone

Figure16. WELD BEAD ZONE OF SAMPLE B4

PROJECT REPORT


3. PROPERTIES OF WELD ZONE OF WELD BEAD (B5)

A) Grain size is smaller then the B4 (WELD BEAD)

B) Width of columns smaller than the B4 (WELD BEAD) due to increase heat input

C) Grain boundary ferrite decreased due to increased heat input

D) Polygonal ferrite less than the B4 (WELD BEAD) due to increased heat input.

E) Acicular ferrite increased due to increased heat input.

F) Cast structure of Weld Zone B5.

Figure 17. WELD BEAD ZONE OF SAMPLE B5

PROJECT REPORT


4. PROPERTIES OF WELD ZONE OF WELD BEAD B6

A) Width of column decreases due to higher heat input. B) Grain boundary ferrite decreased due to increased heat input C) Grain structure fine D) Polygonal ferrite less than the B3, B4, B5. E) Acicular ferrite increased due to increased heat input, it is desirable it has more toughness and more ductility and more strength than the B3, B4 & B5 F) Cast structure of Weld Zone same as B5.

Figure18. WELD ZONE OF SAMPLE B6

PROJECT REPORT


PROPERTIES OF BASE METAL OF WELD BEAD (B3, B4, AND B5 & B6)

Outside the heat affected zone is the parent metal that was not heated sufficiently to change its microstructure. From the microstructure of base metal of weld bead (B3, B4, B5 & B6) we get uniformly distributed ferrite pearlite grain structure.

PROPERTIES OF HEAT EFFECTED ZONE OF WELD BEAD (B3, B4, B5 & B6)

(1) Adjacent to the weld metal zone is the heat affected zone that is composed of parent metal that is composed of parent metal that did not melt but was heated to a high enough temperature for sufficient period that grain growth occurred. (2) heat- affected zone is the portion of the portion of the base metal whose mechanical properties and microstructure have been altered by the heat of welding (3) The width of HAZ varies according to the welding process and technique: in arc welds it extends only a few mm from the fusion boundary, but in oxy- acetylene and electro slag welds it is some- what wider. NOTE: - The microstructure of HAZ of weld bead B3, B4, B5, & B6 at the given heat input approximately same which is shown in figure

PROJECT REPORT


WORKPIECE SAMPLES

B3 B4

B5 B6

Polished side of the workpieces Top view of the workpieces

Figure19. The welded metal specimens over which the microstructures are being analysed.

PROJECT REPORT


Table 2.VARIABLE PARAMETERS FOR WELDING

Table3. WELDING PARAMETERS FOR THE FOUR WORKPIECES

S.No

W(Weld Current)

V (Voltag

S(Speed)

B3

300 amp

32 Volts

0.5 meter/min

B4

500 amp

2 Volts

0.5 meter/min

B5

300 amp

40 Volts

0.25 meter/min

B6 500 amp

40 Volts 0.25 meter/min

Formula Used For Finding Heat Input (H) H = {(V*W/S)* 60}/ 106

*

Value

η Kilo joule/mm Where η = Efficiency of SAW Welding Process (η is 0.9) V = Voltage W = Weld Current (in Amperes) S = Speed (in meter/min)

Weld Current (W)

Voltage (V)

Speed (S)

(Higher Value)

500 amp (A)

40 Volts (V)

0.5 meter/min

(Lower Value

300 amp (A)

32 Volts (V)

0.25 meter/min

PROJECT REPORT


CALCULATIONS FOR FINDING HEAT INPUT OF FOUR DIFFERENT WELD BEADS

1) FOR B3 WELD BEAD W=300amp, V= 32 volt, S=0.5 meter/min For using heat input formula

H = {(V*W/S)* 60}/ 106 * η Kilo joule/mm

Where η = Efficiency of SAW Welding Process (η is 0.9)

H= {(300*32*60)/ 106

*0.5}*0.9 = 1.035 Kilo joule/mm (Minimum heat Input)

2) FOR B4 WELD BEAD W=500amp, V= 32 volt, S=0.5 meter/min For using heat input formula

H = {(V*W/S)* 60}/ 106 * η Kilo joule /mm


H= {(500*32*60)/ 106*0.5}*0.9 = 1.728 Kilo joule/mm

PROJECT REPORT


3) FOR B5 WELD BEAD

W=300amp, V= 40 volt, S=0.25 meter/min For using heat input formula



H= {(300*40*60)/ 106

*0.25}*0.9 = 2.592 Kilo joule /mm

4) FOR B6 WELD BEAD W=500amp , V= 40 volt , S=0.25 meter/min For using heat input formula



H= {(500*40*60)/ 106*0.25}*0.9 = 4.32 Kilo joule/mm ( Maximum heat Input )

PROJECT REPORT


MMAATTLLAABB

PPRROOGGRRAAMMMMEE

PROJECT REPORT


MATLAB PROGRAMME: -

COLOR IMAGE PROGRAMME: -

file=input('Enter the name of file in single Quatation ');

I = imread(file);

background = imopen(I,strel('disk',15));

% Display the Background Approximation as a Surface

figure, surf(double(background(1:8:end,1:8:end))),zlim([0 255]);

set(gca,'ydir','reverse');

I2 = imsubtract(I,background);

level = graythresh(I2);

bw = im2bw(I2,level);

[labeled,numObjects] = bwlabel(bw,4); % 4-connected

rect = [105 125 10 10];

grain = imcrop(labeled,rect);

RGB_label = label2rgb(labeled, @spring, 'c', 'shuffle');

imshow(RGB_label)

PROJECT REPORT


IMAGE VALUE PROGRAMME: -

file=input('Enter the name of file in single Quatation ');

im=imread(file);

[xim,yim,zim]=size(im);

mark=impixel(im);

[xmr,ymr]=size(mark);

val=60;

for i = 1:xmr,

count(i)=0;

for j = 1:xim,

for k = 1:yim,

if ((im(j,k,1) < mark(i,1)+val) & (im(j,k,1) > mark(i,1)-val)) & ...

((im(j,k,2) < mark(i,2)+val) & (im(j,k,2) > mark(i,2)-val)) & ...

((im(j,k,3) < mark(i,3)+val) & (im(j,k,3) > mark(i,3)-val))

count(i)=count(i)+1;

end

end

end

end

PROJECT REPORT


padd=0;

cadd=0;

total=xim*yim;

for i = 1:xmr,

cadd=cadd+count(i);

% percentage(i)=count(i)/total*100;

% padd=padd+percentage(i);

end

cadd

for i = 1:xmr,

% cadd=cadd+count(i);

percentage(i)=(count(i)/cadd)*100;

padd=padd+percentage(i);

end

padd;

percentage

pie(percentage);

legend('Blue-AF','pink-GF','yellow-PF','red','brown');

count

clear all;

PROJECT REPORT


RREESSUULLTT

&&

DDIISSCCUUSSSSIIOONN

PROJECT REPORT


RESULT & DISCUSSION: -

B3 WELD BEAD: -

Figure20. Microstructure of weld bead before image processing

Figure21. Microstructure of weld bead after image processing

PROJECT REPORT


Phases

Figure22. Percentage distribution of phases in microstructure

Percentage

Acicular ferrite 47.538

Grain boundary ferrite 39.948

Polygonal ferrite 4.700

others 7.814

PROJECT REPORT


B4 WELD BEAD: -

.



PROJECT REPORT


Phases


Percentage




others 14.942

PROJECT REPORT


B5 WELD BEAD: -



PROJECT REPORT


Phases


Percentage




others 25.907

PROJECT REPORT


B6 WELD BEAD: -



PROJECT REPORT


Phases


Percentage




others 24.292

PROJECT REPORT


CCOONNCCLLUUSSIIOONN

PROJECT REPORT


CONCLUSION: -

(A). FOR B3 WELD ZONE:-

1. Percentage of acicular ferrite is about 47% at lower heat input 1.035Kilo joule/mm

2. Percentage of grain boundary ferrite 40% at lower heat input it is not desirable, because for better strength

& toughness percentage of grain boundary ferrite is low

3. Percentage of polygonal ferrite 5% it is also not desirable. Because It is detrimental to toughness &

strength

4. Percentage of rest phases is about 8% .it is less effective then above

5. Our main target is comparison of acicular ferrite percentage between B3, B4, and B5 & B6 weld bead. If

percentage of acicular ferrite is more Strength, toughness, & ductility

(B) FOR B4 WELD ZONE:-

1. Percentage of acicular ferrite is about 52% at heat input 1.728 Kilo joule/mm more than the B3 weld

bead. Means if we increase the heat input Percentage of acicular ferrite increases.

2. Percentage of grain boundary ferrite 23% at above heat input it is not desirable, because for better

strength & toughness percentage of grain boundary ferrite is low. In B4 weld bead percentage decreases due

to higher heat input than B3 weld bead


strength


5. Comparison of acicular ferrite percentage between B3 & B4 we get if we increase the heat input then

percentage of acicular ferrite increases & percentage of grain boundary ferrite decreases. It is desirable for

better strength & toughness, ductility

PROJECT REPORT


(C) FOR B5 WELD ZONE:-



2. Percentage of grain boundary ferrite 6% at above heat input it is not desirable, because for better strength

& toughness percentage of grain boundary ferrite is low. In B5 weld bead percentage decreases due to

higher heat input than B4weld bead


strength


5. Comparison of acicular ferrite percentage between B3 , B4 &B5 we get if we increase the heat input then

percentage of acicular ferrite increases & percentage of grain boundary ferrite decreases. It is desirable for

better strength & toughness, ductility

(D) FOR B6 WELD ZONE:-



2. Percentage of grain boundary ferrite 6% at above heat input it is not desirable, because for better strength

& toughness percentage of grain boundary ferrite is low. In B6 weld bead percentage decreases due to

higher heat input than B4weld bead


strength


5. Comparison of acicular ferrite percentage between B3, B4, and B5 &B6 we get if we increase the heat

input then percentage of acicular ferrite increases & percentage of grain boundary ferrite decreases. It is

desirable for better strength & toughness, ductility

PROJECT REPORT


REFERENCE

PROJECT REPORT


Reference

1:- Kulwant Singh, 2006. “Some studies in recycling of submerged arc welding slag as a flux.” Thesis

of Doctor of Philosophy 2006.

2:- Prashanta Kanjilal, Sujit Kumar Majumdar and Tapan Kumar Pal, 2005. “Predication of acicular

ferrite from flux ingredients in submerged arc welds metal of C-Mn steel.” Isij international, vol.45.

3:- B.Basu and R.Raman, 2002. “Micro structural variation in a high strength structural steel weld

under isoheat input conditions.” Welding journal November 2002.

4:- A.Joarder, S.C.Saha and A.K.Ghose,1991. “ Study of submerged arc weld metal and heat affected

zone microstructure of a plain carbon steel.” Welding research supplement june 1991.

5:- C.B. Dallam, S.Liu, and D.L. Olson, 1985. “Flux composition depends of microstructure and

toughness of submerged arc HSLA weldments.” Welding research supplement may 1985.

6: - “A guide to MATLAB for beginners and experienced users” by Brian R Hunt.

7: - www.mathworks.com.

8: - “MATLAB 7.0 basics” by P. Howard.

9: - MATLAB demos and MATLAB help.

10: - “Introduction to MATLAB” by Steven A. Jones.

PROJECT REPORT


11: - The MathWorks, Inc., Getting Started with MATLAB.

http://www.mathworks.com/access/helpdesk/help/techdoc /learn_matlab/learn_matlab.shtml

12: - The MathWorks, Inc., List of Matlab-based books.

http://www.mathworks.com/support/books/index.jsp

13: - “MATLAB an introduction with applications” by Amos Gilat.

14: - “Introduction to MATLAB” by Rudra Pratap.

15: - www.google .com.

16: - www.altavista .com.

METALLURGICAL INVESTIGATION OF MICROSTRUCTURE OF THE WELD BEAD IN SAW WELDING USING THE RECYCLED...

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