metallurgical investigation of microstructure of the weld bead in saw welding using the recycled...
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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
16 WELD ZONE OF SAMPLE B4
17 WELD BEAD ZONE OF SAMPLE B5
18 WELD ZONE OF SAMPLE B6
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
23 Microstructure of B4.weld bead before image processing
24 Microstructure of B4.weld bead before image processing
25 Percentage distribution of phases in microstructure of B4. weld bead
Fig No. Explanation
26 Microstructure of B5.weld bead before image processing
27 Microstructure of B5.weld bead before image processing
28 Percentage distribution of phases in microstructure of B5. weld bead
29 Microstructure of B6.weld bead before image processing
30 Microstructure of B6.weld bead before image processing
31 Percentage distribution of phases in microstructure of B6. weld bead
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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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MMEETTAALLLLUURRGGIICCAALL
IINNVVEESSTTIIGGAATTIIOONN
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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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DEPARTMENT OF MECHANICAL ENGINEERING 25
Figure 11. SAMPLE NO: B3
(A). WELD AND BASE METAL
(B). BASE METAL
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DEPARTMENT OF MECHANICAL ENGINEERING 26
(C). WELD ZONE
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DEPARTMENT OF MECHANICAL ENGINEERING 27
Figure 12. SAMPLE NO: B4
(A). WELD AND BASE METAL
(B). BASE METAL
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DEPARTMENT OF MECHANICAL ENGINEERING 28
(C). WELD ZONE
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DEPARTMENT OF MECHANICAL ENGINEERING 29
Figure 13. SAMPLE NO: B5
(A). WELD AND BASE METAL
(B). BASE METAL
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DEPARTMENT OF MECHANICAL ENGINEERING 30
(C). WELD ZONE
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DEPARTMENT OF MECHANICAL ENGINEERING 31
Figure 14. SAMPLE NO: B6
(A). WELD AND BASE METAL
(B). BASE METAL
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DEPARTMENT OF MECHANICAL ENGINEERING 32
(C). WELD ZONE
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DEPARTMENT OF MECHANICAL ENGINEERING 33
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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DEPARTMENT OF MECHANICAL ENGINEERING 34
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
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DEPARTMENT OF MECHANICAL ENGINEERING 35
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
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DEPARTMENT OF MECHANICAL ENGINEERING 36
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
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DEPARTMENT OF MECHANICAL ENGINEERING 37
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
DEPARTMENT OF MECHANICAL ENGINEERING 38
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
DEPARTMENT OF MECHANICAL ENGINEERING 39
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
DEPARTMENT OF MECHANICAL ENGINEERING 40
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
Where η = Efficiency of SAW Welding Process (η is 0.9)
H= {(500*32*60)/ 106*0.5}*0.9 = 1.728 Kilo joule/mm
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DEPARTMENT OF MECHANICAL ENGINEERING 41
3) FOR B5 WELD BEAD
W=300amp, V= 40 volt, S=0.25 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*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 = {(V*W/S)* 60}/ 106 * η Kilo joule/mm
Where η = Efficiency of SAW Welding Process (η is 0.9)
H= {(500*40*60)/ 106*0.25}*0.9 = 4.32 Kilo joule/mm ( Maximum heat Input )
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DEPARTMENT OF MECHANICAL ENGINEERING 42
MMAATTLLAABB
PPRROOGGRRAAMMMMEE
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DEPARTMENT OF MECHANICAL ENGINEERING 43
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)
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DEPARTMENT OF MECHANICAL ENGINEERING 44
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
DEPARTMENT OF MECHANICAL ENGINEERING 45
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;
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DEPARTMENT OF MECHANICAL ENGINEERING 46
RREESSUULLTT
&&
DDIISSCCUUSSSSIIOONN
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 47
RESULT & DISCUSSION: -
B3 WELD BEAD: -
Figure20. Microstructure of weld bead before image processing
Figure21. Microstructure of weld bead after image processing
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 48
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
DEPARTMENT OF MECHANICAL ENGINEERING 49
B4 WELD BEAD: -
.
Figure23. Microstructure of weld bead before image processing
Figure24. Microstructure of weld bead after image processing
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 50
Phases
Figure25. Percentage distribution of phases in microstructure
Percentage
Acicular ferrite 53.439
Grain boundary ferrite 23.827
Polygonal ferrite 7.792
others 14.942
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DEPARTMENT OF MECHANICAL ENGINEERING 51
B5 WELD BEAD: -
Figure26. Microstructure of weld bead before image processing
Figure27. Microstructure of weld bead after image processing
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DEPARTMENT OF MECHANICAL ENGINEERING 52
Phases
Figure28. Percentage distribution of phases in microstructure
Percentage
Acicular ferrite 63.109
Grain boundary ferrite 5.698
Polygonal ferrite 5.286
others 25.907
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 53
B6 WELD BEAD: -
Figure29. Microstructure of weld bead after image processing
Figure30. Microstructure of weld bead after image processing
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 54
Phases
Figure31. Percentage distribution of phases in microstructure
Percentage
Acicular ferrite 63.729
Grain boundary ferrite 5.990
Polygonal ferrite 5.989
others 24.292
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 55
CCOONNCCLLUUSSIIOONN
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 56
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
3. Percentage of polygonal ferrite 7% it is also not desirable. Because It is detrimental to toughness &
strength
4. Percentage of rest phases is about 16% .it is less effective then above
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
DEPARTMENT OF MECHANICAL ENGINEERING 57
(C) FOR B5 WELD ZONE:-
1. Percentage of acicular ferrite is about 64% at heat input 2.592 Kilo joule/mm more than the B4 weld
bead. Means if we increase the heat input Percentage of acicular ferrite increases.
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
3. Percentage of polygonal ferrite 6% it is also not desirable. Because It is detrimental to toughness &
strength
4. Percentage of rest phases is about 23% .it is less effective then above
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:-
1. Percentage of acicular ferrite is about 64% at heat input 2.592 Kilo joule/mm more than the B4 weld
bead. Means if we increase the heat input Percentage of acicular ferrite increases.
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
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 25% .it is less effective then above
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
DEPARTMENT OF MECHANICAL ENGINEERING 58
REFERENCE
PROJECT REPORT
DEPARTMENT OF MECHANICAL ENGINEERING 59
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
DEPARTMENT OF MECHANICAL ENGINEERING 60
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.