Machine vision amk mtmr final

B.E.PROJECTS
CONTACT 9444863248
chock1963@gmail.com
AUTOMATIC ON LINE INSPECTION OF
MACHINING COMPONENTS USING MACHINE
VISION

AUTOMATIC ON LINE INSPECTION OF
MACHINING COMPONENTS USING MACHINE
VISION
Submitted in the partial fulfillment of the requirement for the award of
“DIPLOMA IN MECHANCIAL ENGINEERING (MTMR)”
SUBMITTED BY:
1. A.MANIKANDAN 4. S.NANDAKUMAR
2. M.ARUNKUMAR 5.S.RAMESH KUMAR
3. N.JEEVAKUMAR 6. S.MUTHUKUMAR

Under guidance of
Mr. A.CHOCKALINGAM, M.E.,
OCTOBER 2014.
DEPARTMENT OF MECHANICAL ENGINEERING (MTMR)
A M K TECHNOLOGICAL POLYTECHNIC COLLEGE
CHEM BARAMBAKKAM, CHENNAI – 602 103
A M K TECHNOLOGICAL POLYTECHNIC COLLEGE
CHEM BARAMBAKKAM, CHENNAI – 602 103
BONAFIDE CERTIFICATE
This is to certify that this Project work on
“AUTOMATIC ON LINE INSPECTION OF MACHINING
COMPONENTS USING MACHINE VISION”
submitted by …………………… ……………. Reg. No. ……………
in partial fulfillment for the award of
DIPLOMA IN MECHANICAL ENGINEERING (MTMR)
This is the bonafide record of work carried out by him under our supervision
during the year 2014
Submitted for the Viva-voce exam held on ……………..

H.O.D PROJECT GUIDE
INTERNAL EXAMINER EXTERNAL EXAMINER
ACKNOWLEDGEMENT
At the outset, we would like to emphasize our sincere thanks to the
Principal Mr. VIJAY KISHORE, M.TECH., MISTE.., encouragement
and valuable advice.
we thank our Esquired Head of Department Mr R. RAJKUMAR,
A.M.I.E, M.E., for presenting his felicitations on us.
We are grateful on our Entourages Mr. A.CHOCKALINGAM,
M.E., for guiding in various aspects of the project making it a grand success.
We also owe our sincere thanks to all staff members of the
Mechanical Engineering (MTMR) Department.

Ultimately, we extend our thanks to all who had rendered their co-operation
for the success of the project.
CONTENTS

CONTENTS
CHAPTER NO. TITLE
1. INTRODUCTION
2. SYNOPSIS
3. CONSTRUCTION
4. WORKING PRINCIPLE
5. ELECTRICAL CIRCUIT DETAILS
6. INTRODUCTION TO MACHINE VISION
7. MECHANIAL ASSEMBLY DIAGRAM

8. PNEUMATIC COMPONENTS DETAILS
9. ELECTRICAL WIRING DIAGRAM
10. COST ESTIMATION
11. CONCLUSION
12. BIBLIOGRAPHY
INTRODUCTION

INTRODUCTION
In our technical education the project work plays a major role. Every
students is put in to simulated life particularly where the student required to
bring his knowledge, skill and experience of the project work.
It helps how to evolve specifications under given constrains by
systematic approach to the problem a construct a work device. Project work
thus integrates various skills and knowledge attainment during study and
gives orientation towards application.

As the students solve the various problems exposed by the project
work, the students get the confidence to overcome such problems in the
future life. It helps in expanding the thinking and alternatives for future
applications.
SYNOPSIS

SYNOPSIS
The main aim for us to select this Project work is to acquire practical
knowledge in the field of machine vision based automation. The
technology is improving in a tremendous manner that a new technology
today is an old or obsolete after a short period of time. In any industrial
application aiming for automation to increase the production and thus to
reduce the cost of unit.
In our project “AUTOMATIC INSPECTION OF MACHINING
COMPONENTS USING MACHINE VISION’ the machining components
are transported in a belt conveyor for inspecting their number of holes
by taking image through the camera and analysed using image
processing software like Matlab . The defected components are ejected
by the pneumatic cylinder controlled via computer and microcontroller
based control system .
ADVANTAGES;

· This system is used to develop industrial automation
and assist with CIM environment.
· It promote the unmanned industry.
· Reduces waste motions which cause fatigues to
worker.
· It reduces labour cost

Objective
· To check the quality of the material from the raw material to
final product point automatically
· To develop industrial automation and assist with CIM
environment.
· To promote the unmanned industry.

Project Background
In the present global rationalization and competitive world most of the
industries set up unmanned industry in order to eliminate labor cost
and to increase productivity.
Project Elements
• Fabrication unit
DC motor drive for conveyor belt movement
• Double acting cylinder( 1 no)
• 5/2 way solenoid operated directional control valve(1no)
• Flow control valve

CONSTRUCTION
CONSTRUCTION
This project consists of following parts
1. M.S. Fabricated base stand
2.Pneumatic system components .

3.camera to capture the images
4. matlab software with PC
5. Belt conveyor material transferring system
6. microcontroller based control unit
7. Interfacing card for camera, Controller and PC
AIR CYLINDER
AIR cylinder is pneumatic equipment. These cylinder are used for
sliding horizontal movement for ejecting defected components. The
cylinder and piston Rod is engaged in single solid unit. Air is supplied to
cylinder in A+ and A- position. Movement of slide is depending upon the
pressure of air.
In Pneumatic system, the piston rod in the double acting air cylinder of 25
mm diameter and 100 mm length is actuated by the supply of compressed air
which is supplied through the 5/2 way solenoid operated directional control
valve. The air cylinder ports A and B ports are connected to the 5/2 way
Directional control. Valve with 6/8mm polyurethane tube. The 6mm
connector is used to connect this air cylinder ports and D.C valve. The
minimum air pressure required is 5 to 6bar.
Air cylinder 25 mm DIA x 200mm L size;

5/2 WAY SOLENOID VALVE;
The Pneumatic circuit diagram for the pneumatic system is shown in
below.

FLOW CONTROL VALVE;
This flow control valve is used to control the speed of the piston
movement in the cylinder. Two flow control valves are mounted on each
port of A and B of the cylinder jack unit.The below figure shows the flow
control valve.
1. M.S.STAND:
The M.S.Stand is shown in figure. It is made in M.S. material having
600 mm height. This unit has DC motor drive belt conveyor mechanism..
The cylinder is mounted in horizontal position on the conveyor stand. The

camera is held rigidly above conveyor . The IR sensor mounted at the
starting point of material flow in the conveyor.
5.CONVEYOR MECHANISM;
This conveyor is used to transfer the jobs continuously towards the
machining. The jobs are placed under the belt conveyor .The conveyor belt
is rotated between the driving and driven shafts by the DC motor. The DC
motor and the conveyor belt assembly is mounted on the fabricated
stand..The belt conveyor mechanism is shown in below fig.
ASSEMBLY DIAGRAM OF BELT CONVEYOR MECHANISM
DC Motor:
The DC motor is used to drive the conveyor belt. The motor works
in 24V D.C. supply and it rotates. The current rating is 750 milli amps and
it is a SHUNT motor having 3 kg torque.

WORKING PRINCIPLE
WORKING PRINCIPLE

The function of the controller system shown below.
ST
Controller
unit
24DC motor
BELT CONVEYOR
START
STOP
IR
SENSOR
matlab /PC
SIGNAL
CAMERA
12VDC SOLENOID
VALVE
EJECTOR
Initially the job to be checked is passed in the beltconveyor . An I R sensor
is mounted at the ¼ th distance of the belt conveyor. This IR sensor sends
the signal to the controller which switch on the camera and images are
analysed with MATLAB software . The IR sensor is used for detecting the

presence of any material object in the conveyor. the matlab software
compares the image of the component to be checked with the quality of
original quality component data and gives an output signal to the controller.
the controller components are passed to the other end and the defected
components are ejected by the cylinder by the signal given by the controller
system..

INTRODUCTION
TO
COMPUTER VISION

COMPUTER VISION
Introduction
Computer vision is the study and application of methods which allow
computers to "understand" image content or content of multidimensional
data in general. The term "understand" means here that specific information
is being extracted from the image data for a specific purpose: either for
presenting it to a human operator (e. g., if cancerous cells have been detected
in a microscopy image), or for controlling some process (e. g., an industry
robot or an autonomous vehicle). The image data that is fed into a computer
vision system is often a digital gray-scale or colour image, but can also be in
the form of two or more such images (e. g., from a stereo camera pair), a
video sequence, or a 3D volume (e. g., from a tomography device). In most
practical computer vision applications, the computers are pre-programmed to
solve a particular task, but methods based on learning are now becoming
increasingly common. Computer vision can also be described as the
complement (but not necessary the opposite) of biological vision. In
biological vision and visual perception real vision systems of humans and
various animals are studied, resulting in models of how these systems are
implemented in terms of neural processing at various levels.
State Of The Art

Relation between Computer vision and various other fields
The field of computer vision can be characterized as immature and diverse.
Even though earlier work exists, it was not until the late 1970's that a more
focused study of the field started when computers could manage the
processing of large data sets such as images. However, these studies usually
originated from various other fields, and consequently there is no standard
formulation of the "computer vision problem". Also, and to an even larger
extent, there is no standard formulation of how computer vision problems
should be solved. Instead, there exists an abundance of methods for solving
various well-defined computer vision tasks, where the methods often are
very task specific and seldom can be generalized over a wide range of
applications. Many of the methods and applications are still in the state of
basic research, but more and more methods have found their way into
commercial products, where they often constitute a part of a larger system
which can solve complex tasks (e.g., in the area of medical images, or
quality control and measurements in industrial processes).
A significant part of artificial intelligence deals with planning or deliberation
for system which can perform mechanical actions such as moving a robot
through some environment. This type of processing typically needs input
data provided by a computer vision system, acting as a vision sensor and
providing high-level information about the environment and the robot. Other
parts which sometimes are described as belonging to artificial intelligence
and which are used in relation to computer vision is pattern recognition and
learning techniques. As a consequence, computer vision is sometimes seen
as a part of the artificial intelligence field.

Since a camera can be seen as a light sensor, there are various methods in
computer vision based on correspondences between a physical phenomenon
related to light and images of that phenomenon. For example, it is possible
to extract information about motion in fluids and about waves by analyzing
images of these phenomena. Also, a subfield within computer vision deals
with the physical process which given a scene of objects, light sources, and
camera lenses forms the image in a camera. Consequently, computer vision
can also be seen as an extension of physics.A third field which plays an
important role is neurobiology, specifically the study of the biological vision
system. Over the last century, there has been an extensive study of eyes,
neurons, and the brain structures devoted to processing of visual stimuli in
both humans and various animals. This has led to a coarse, yet complicated,
description of how "real" vision systems operate in order to solve certain
vision related tasks. These results have led to a subfield within computer
vision where artificial systems are designed to mimic the processing and
behaviour of biological systems, at different levels of complexity. Also,
some of the learning-based methods developed within computer vision have
their background in biology.
Yet another field related to computer vision is signal processing. Many
existing methods for processing of one-variable signals, typically temporal
signals, can be extended in a natural way to processing of two-variable
signals or multi-variable signals in computer vision. However, because of
the specific nature of images there are many methods developed within
computer vision which have no counterpart in the processing of one-variable
signals. A distinct character of these methods is the fact that they are non-

linear which, together with the multi-dimensionality of the signal, defines a
subfield in signal processing as a part of computer vision.
Beside the above mentioned views on computer vision, many of the related
research topics can also be studied from a purely mathematical point of
view. For example, many methods in computer vision are based on statistics,
optimization or geometry. Finally, a significant part of the field is devoted to
the implementation aspect of computer vision; how existing methods can be
realized in various combinations of software and hardware, or how these
methods can be modified in order to gain processing speed without losing
too much performance.
Related Fields
Computer vision, Image processing, Image analysis, Robot vision and
Machine vision are closely related fields. If you look inside text books
which have either of these names in the title there is a significant overlap in
terms of what techniques and applications they cover. This implies that the
basic techniques that are used and developed in these fields are more or less
identical, something which can be interpreted as there is only one field with
different names. On the other hand, it appears to be necessary for research
groups, scientific journals, conferences and companies to present or market
themselves as belonging specifically to one of these fields and, hence,
various characterizations which distinguish each of the fields from the others
have been presented. The following characterizations appear relevant but
should not be taken as universally accepted.

Image processing and Image analysis tend to focus on 2D images, how to
transform one image to another, e.g., by pixel-wise operations such as
contrast enhancement, local operations such as edge extraction or noise
removal, or geometrical transformations such as rotating the image. This
characterization implies that image processing/analysis neither require
assumptions nor produce interpretations about the image content.
Computer vision tends to focus on the 3D scene projected onto one or
several images, e.g., how to reconstruct structure or other information about
the 3D scene from one or several images. Computer vision often relies on
more or less complex assumptions about the scene depicted in an image.
Machine vision tends to focus on applications, mainly in industry, e.g.,
vision based autonomous robots and systems for vision based inspection or
measurement. This implies that image sensor technologies and control
theory often are integrated with the processing of image data to control a
robot and that real-time processing is emphasized by means of efficient
implementations in hardware and software. There is also a field called
Imaging which primarily focus on the process of producing images, but
sometimes also deals with processing and analysis of images. For example,
Medical imaging contains lots of work on the analysis of image data in
medical applications.
Finally, pattern recognition is a field which uses various methods to extract
information from signals in general, mainly based on statistical approaches.
A significant part of this field is devoted to applying these methods to image
data.A consequence of this state of affairs is that you can be working in a lab
related to one of these fields, apply methods from a second field to solve a

problem in a third field and present the result at a conference related to a
fourth field!
Typical Tasks Of Computer Vision
Each of the application areas described above employ a range of computer
vision tasks; more or less well-defined measurement problems or processing
problems, which can be solved using a variety of methods. Some examples
of typical computer vision tasks are presented below.
Recognition
The classical problem in computer vision, image processing and machine
vision is that of determining whether or not the image data contains some
specific object, feature, or activity. This task can normally be solved
robustly and without effort by a human, but is still not satisfactory solved in
computer vision for the general case: arbitrary objects in arbitrary situations.
The existing methods for dealing with this problem can at best solve it only
for specific objects, such as simple geometric objects (e.g., polyhedrons),
human faces, printed or hand-written characters, or vehicles, and in specific
situations, typically described in terms of well-defined illumination,
background, and pose of the object relative to the camera.
Different varieties of the recognition problem are described in the literature:
· Recognition: one or several pre-specified or learned objects or object
classes can be recognized, usually together with their 2D positions in
the image or 3D poses in the scene.

· Identification: An individual instance of an object is recognized.
Examples: identification of a specific person face or fingerprint, or
identification of a specific vehicle.
· Detection: the image data is scanned for a specific condition.
Examples: detection of possible abnormal cells or tissues in medical
images or detection of a vehicle in an automatic road toll system.
Detection based on relatively simple and fast computations is
sometimes used for finding smaller regions of interesting image data
which can be further analyzed by more computationally demanding
techniques to produce a correct interpretation. Several specialized
tasks based on recognition exist, such as:
· Content-based image retrieval: find all images which has a specific
content in a larger set or database of images.
· Pose estimation: estimation of the position and orientation of specific
object relative to the camera. Example: to allow a robot arm to pick up
the objects from the belt.
· Optical character recognition (or OCR): images of printed or
handwritten text are converted to computer readable text such as
ASCII or Unicode.
Motion
Several tasks relate to motion estimation in which an image sequence is
processed to produce an estimate of the local image velocity at each point.
Examples of such tasks are
· Egomotion: determine the 3D rigid motion of the camera.

· Tracking of one or several objects (e.g. vehicles or humans) through
the image sequence.
· Surveillance: detection of possible activities based on motion.
Scene Reconstruction
Given two or more images of a scene, or a video, scene reconstruction aims
at computing a 3D model of the scene. In the simplest case the model can be
a set of 3D points. More sophisticated methods produce a complete 3D
surface model.
Image Restoration
Given an image, an image sequence, or a 3D volume, which has been
degraded by noise, image restoration aims at producing the image data
without the noise. Examples of noise processes which are considered are
sensor noise (e.g., ultrasonic images) and motion blur (e.g., because of a
moving camera or moving objects in the scene).
Computer Vision Systems
A typical computer vision system can be divided in the following
subsystems:
Image acquisition
The image or image sequence is acquired with an imaging system
(camera,radar,lidar,tomography system). Often the imaging system has to be
calibrated before being used.

Preprocessing
In the preprocessing step, the image is being treated with "low-level"-
operations. The aim of this step is to do noise reduction on the image (i.e. to
dissociate the signal from the noise) and to reduce the overall amount of
data. This is typically being done by employing different (digital)image
processing methods such as:
1. Downsampling the image.
2. Applying digital filters
3. Computing the x- and y-gradient (possibly also the time-gradient).
4. Segmenting the image.
a. Pixelwise thresholding.
5. Performing an eigentransform on the image
a. Fourier transform
6. Doing motion estimation for local regions of the image (also known
as optical flow estimation).
7. Estimating disparity in stereo images.
8. Multiresolution analysis
Feature extraction
The aim of feature extraction is to further reduce the data to a set of features,
which ought to be invariant to disturbances such as lighting conditions,
camera position, noise and distortion. Examples of feature extraction are:

1. Performing edge detection or estimation of local orientation.
2. Extracting corner features.
3. Detecting blob features.
4. Extracting spin images from depth maps.
5. Extracting geons or other three-dimensional primitives, such as
superquadrics.
6. Acquiring contour lines and maybe curvature zero crossings.
7. Generating features with the Scale-invariant feature transform.
8. Calculating the Co-occurrence matrix of the image or sub-images
to measure texture.
Registration
The aim of the registration step is to establish correspondence between the
features in the acquired set and the features of known objects in a model-database
and/or the features of the preceding image. The registration step
has to bring up a final hypothesis. To name a few methods:
1. Least squares estimation
2. Hough transform in many variations
3. Geometric hashing
4. Particle filtering
Applications Of Computer Vision
The following is a non-complete list of applications which are studied in
computer vision. In this category, the term application should be interpreted
as a high level function which solves a problem at a higher level of

complexity. Typically, the various technical problems related to an
application can be solved and implemented in different ways.
Applications Of Computer Vision
A facial recognition system is a computer-driven application for
automatically identifying a person from a digital image. It does that by
comparing selected facial features in the live image and a facial database. It
is typically used for security systems and can be compared to other
biometrics such as fingerprint or eye iris recognition systems.
Popular recognition algorithms include eigenface, fisherface, the Hidden
Markov model, and the neuronal motivated Dynamic Link Matching. A
newly emerging trend, claimed to achieve previously unseen accuracies, is
three-dimensional face recognition. Another emerging trend uses the visual
details of the skin, as captured in standard digital or scanned images. Tests
on the FERET database, the widely used industry benchmark, showed that
this approach is substantially more reliable than previous algorithms.
Polly (robot)
Polly was a robot created at the MIT Artificial Intelligence Laboratory by
Ian Horswill for his PhD, which was published in 1993 as a technical report.
It was the first mobile robot to move at animal-like speeds (1m per second)
using computer vision for its navigation. It was an example of behavior
based robotics. For a few years, Polly was able to give tours of the AI
laboratory's seventh floor, using canned speech to point out landmarks such
as Anita Flynn's office. The Polly algorithm is a way to navigate in a
cluttered space using very low resolution vision to find uncluttered areas to

move forward into, assuming that the pixels at the bottom of the frame (the
closest to the robot) show an example of an uncluttered area. Since this
could be done 60 times a second, the algorithm only needed to discriminate
three categories: telling the robot at each instant to go straight, towards the
right or towards the left.
Mobile robot
Mobile Robots are automatic machines that are capable of movement in a
given environment. Robots generally fall into two classes, linked
manipulators (or Industrial robots) and mobile robots. Mobile robots have
the capability to move around in their environment and are not fixed to one
physical location. In contrast, industrial manipulators usually consist of a
jointed arm and gripper assembly (or end effector) that is attached to a fixed
surface.
The most common class of mobile robots are wheeled robots. A second class
of mobile robots includes legged robots while a third smaller class includes
aerial robots, usually referred to as unmanned aerial vehicles (UAVs).
Mobile robots are the focus of a great deal or current research and almost
every major university has one or more labs that focus on mobile robot
research. Mobile robots are also found in industry, military and security
environments, and appear as consumer products.
Robot
A humanoid robot manufactured by Toyota "playing" a trumpet

The word robot is used to refer to a wide range of machines, the common
feature of which is that they are all capable of movement and can be used to
perform physical tasks. Robots take on many different forms, ranging from
humanoid, which mimic the human form and way of moving, to industrial,
whose appearance is dictated by the function they are to perform. Robots can
be grouped generally as mobile robots (eg. autonomous vehicles),
manipulator robots (eg. industrial robots) and Self reconfigurable robots,
which can conform themselves to the task at hand.
Robots may be controlled directly by a human, such as remotely-controlled
bomb-disposal robots, robotic arms, or shuttles, or may act according to their
own decision making ability, provided by artificial intelligence. However,
the majority of robots fall in-between these extremes, being controlled by
pre-programmed computers. Such robots may include feedback loops such
that they can interact with their environment, but do not display actual
intelligence.
The word "robot" is also used in a general sense to mean any machine which
mimics the actions of a human (biomimicry), in the physical sense or in the
mental sense.It comes from the Czech and Slovak word robota, labour or
work (also used in a sense of a serf). The word robot first appeared in Karel
Čapek's science fiction play R.U.R. (Rossum's Universal Robots) in 1921.

Smart Camera
A smart camera is an integrated machine vision system which, in addition
to image capture circuitry, includes a processor, which can extract
information fromimageswithout need for an external processing unit, and
interface devices used to make results available to other devices.
A Smart Camera or „intelligent Camera“ is a self-contained, standalone
vision system with built-in image sensor in the housing of an industrial
video camera. It contains all necessary communication interfaces, e.g.
Ethernet. It is not necessarily larger than an industrial or surveillance
camera. This architecture has the advantage of a more compact volume
compared to PC-based vision systems and often achieves lower cost, at the
expense of a somewhat simpler (or missing altogether) user interface.

Early smart camera (ca. 1985, in red) with an 8MHz Z80 compared to a
modern device featuring Texas Instruments' C64 @1GHz. A Smart Camera
usually consists of several (but not necessarily all) of the following
components:
1. Image sensor (matrix or linear, CCD- or CMOS)
2. Image digitization circuitry
3. Image memory
4. Communication interface (RS232, Ethernet)
5. I/O lines (often optoisolated)
6. Lens holder or built in lens (usually C or C-mount)
Examples Of Applications For Computer Vision
Another way to describe computer vision is in terms of applications areas.
One of the most prominent application fields is medical computer vision or
medical image processing. This area is characterized by the extraction of
information from image data for the purpose of making a medical diagnosis
of a patient. Typically image data is in the form of microscopy images, X-ray
images, angiography images, ultrasonic images, and tomography images.
An example of information which can be extracted from such image data is
detection of tumours, arteriosclerosis or other malign changes. It can also be
measurements of organ dimensions, blood flow, etc. This application area
also supports medical research by providing new information, e.g., about the
structure of the brain, or about the quality of medical treatments.
A second application area in computer vision is in industry. Here,
information is extracted for the purpose of supporting a manufacturing
process. One example is quality control where details or final products are

being automatically inspected in order to find defects. Another example is
measurement of position and orientation of details to be picked up by a robot
arm. See the article on machine vision for more details on this area.
Military applications are probably one of the largest areas for computer
vision, even though only a small part of this work is open to the public. The
obvious examples are detection of enemy soldiers or vehicles and guidance
of missiles to a designated target. More advanced systems for missile
guidance send the missile to an area rather than a specific target, and target
selection is made when the missile reaches the area based on locally
acquired image data. Modern military concepts, such as "battlefield
awareness,"imply that various sensors, including image sensors, provide a
rich set of information about a combat scene which can be used to support
strategic decisions. In this case, automatic processing of the data is used to
reduce complexity and to fuse information from multiple sensors to increase
reliability.
Artist's Concept of Rover on Mars. Notice the stereo cameras mounted on
top of the Rover. (credit: Maas Digital LLC) One of the newer application
areas is autonomous vehicles, which include submersibles, land-based
vehicles (small robots with wheels, cars or trucks), and aerial vehicles. An
unmanned aerial vehicle is often denoted UAV. The level of autonomy
ranges from fully autonomous (unmanned) vehicles to vehicles where
computer vision based systems support a driver or a pilot in various
situations. Fully autonomous vehicles typically use computer vision for
navigation, e. g., a UAV looking for forest fires. Examples of supporting
system are obstacle warning systems in cars and systems for autonomous
landing of aircraft. Several car manufacturers have demonstrated systems for

autonomous driving of cars, but this technology has still not reached a level
where it can be put on the market.
Software For Computer Vision
Animal
Animal (first implementation: 1988 - revised: 2004) is an interactive
environment for Image processing that is oriented toward the rapid
prototyping, testing, and modification of algorithms. To create ANIMAL
(AN IMage ALgebra), XLISP of David Betz was extended with some new
types: sockets, arrays, images, masks, and drawables. The theoretical
framework and the implementation of the working environment is described
in the paper ANIMAL: AN IMage ALgebra.In the theoretical framework of
ANIMAL a digital image is a boundless matrix. However, in the
implementation it is bounded by a rectangular region in the discrete plane
and the elements outside the region have a constant value. The size and
position of the region in the plane (focus) is defined by the coordinates of
the rectangle. In this way all the pixels, including those on the border, have
the same number of neighbors (useful in local operators, such as digital
filters). Furthermore, pixelwise commutative operations remain
commutative on image level, independently on focus.
OpenCv
OpenCV is an open source computer vision library developed by Intel. The
library is cross-platform, and runs on both Windows and Linux. It focuses
mainly towards real-time image processing. The application areas include

1. Human-Computer Interface (HCI)
2. Object Identification
3. Segmentation and Recognition
4. Face Recognition
5. Gesture Recognition
6. Motion Tracking
Visualization Toolkit (VTK)
Visualization Toolkit (VTK) is an open source, freely available software
system for 3D computer graphics, image processing, and visualization used
by thousands of researchers and developers around the world. VTK consists
of a C++ class library, and several interpreted interface layers including
Tcl/Tk, Java, and Python. Professional support and products for VTK are
provided by Kitware, Inc. VTK supports a wide variety ofvisualization
algorithms including scalar, vector, tensor, texture, and volumetric methods;
and advanced modeling techniques such as implicit modelling, polygon
reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation.
Commercial Computer Vision Systems
Automatix Inc., founded in January 1980, was the first company to market
industrial robots with built-in machine vision. Its founders were Victor
Scheinman, inventor of the Stanford arm; Phillippe Villers, Michael Cronin,
and Arnold Reinhold of Computervision; Jake Dias and Dan Nigro of Data
General; Gordon VanderBrug, of NBS and Norman Wittels of Clark
University.

Automatix Robots at the Robots 1985 show in Detroit, Michigan. Clockwise
from lower left: AID 600, AID 900 Seamtracker, Yaskawa
Motoman.Automatix mostly used robot mechanisms imported from Hitachi
at first and later from Yaskawa and KUKA. It did design and manufacture a
Cartesian robot called the AID-600. The 600 was intended for use in
precision assembly but was adapted for welding use, particularly Tungsten
inert gas welding (TIG), which demands high accuracy and immunity from
the intense electromagnetic interference that the TIG process creates.
Automatix was the first company to market a vision-guided welding robot
called Seamtracker. Structured laser light and monochromatic filters were
used to allow an image to be seen in the presence of the welding arc.
Another concept, invented by Mr. Scheinman, was RobotWorld, a system of
cooperating small modules suspended from a 2-D linear motor. The product
line was later sold to Yaskawa.
Automatix raised large amounts of venture capital, and went public in 1983,
but was not profitable until the early 1990s. In 1994, Automatix merged with
another machine vision company, Itran Corp., to form Acuity Imaging, Inc.
Acuity was acquired by Robotics Vision Systems Inc. (RVSI) in September
1995. As of 2004, RVSI still supported the evolved Automatix machine
vision package under the PowerVision brand.

RapidEye is a commercial multispectral remote sensing satellite mission
being designed and implemented by MDA for RapidEye AG. The RapidEye
sensor images five optical bands in the 400-850nm range and provides 5m
pixel size at nadir. Rapid delivery and short revisit times are provided
through the use of a five-satellite constellation.
Scantron is the name of a United States company that makes and sells
Scantron exam answer sheets and the machines to grade them. The Scantron
system usually takes the form of a "multiple choice, fill-in-the-circle/
square/rectangle" form of varying length and width, from single
column 50 answer tests, to multiple 8.5" x 11" page forms used in
standardized testing such as the SAT and ACT. The forms are sensed
optically, using optical mark recognition to detect markings in each place, in
a "Scantron Machine" that tabulates and can automatically grade results.
Earlier versions were sensed electrically.

A typical 100-answer Scantron answer sheet. This is only half of it (the front
side) with the back side not being shown.Commonly, there are two sides to

Scantron answer sheets. They can contain 50 answer blanks, 100 answer
blanks, and so on. There is even a smaller form called a "Quiz Strip" that
contains only about 20 answer boxes to bubble-in. On the larger sheets, there
is a space on the back where answers can be manually written in for separate
questions, if a test giver issues them out. The full-sized 8.5" x 11" form may
contain a larger area for using it to work on math formulas, write short
answers, etc. Answers "A" and "B" are commonly used for "True" and
"False" questions, as shown in the image to the right on the top of each row.
Grading of Scantron sheets is performed first by creating an answer key. The
answer key is simply a standard Scantron answer sheet with all of the correct
answers filled in, along with the "key" rectangle at the top of the sheet.Once
you have your answer key ready the Scantron machine is powered on and
the answer key is fed through. This stores the answer key in the memory of
the Scantron machine and any further sheets that are fed through will be
graded and marked according to the key in memory. Switching off the
Scantron machine will stop the paper feed and clear the memory.

Conclusion
Computer vision, unlike for example factory machine vision, happens in
unconstrained environments, potentially with changing cameras and
changing lighting and camera views. Also, some “objects” such as roads,
rivers, bushes, etc. are just difficult to describe. In these situations,
engineering a model a-priori can be difficult. With learning-based vision,
one just “points” the algorithm at the data and useful models for detection,
segmentation, and identification can often be formed. Learning can often
easily fuse or incorporate other sensing modalities such as sound, vibration,
or heat. Since cameras and sensors are becoming cheap and powerful and
learning algorithms have a vast appetite for computational threads, Intel is
very interested in enabling geometric and learning-based vision routines in
its OpenCV library since such routines are vast consumers of computational
power.

ADVANTAGES
 It requires simple maintenance cares
 Conveying of parts are done automatically.
 It transfer the parts to the corresponding directions.
 Less skill technicians is sufficient to operate.
 Checking and cleaning are easy, because of the main parts
are screwed.
 Handling is easy.
 Manual power not required
 Repairing is easy.
 Replacement of parts is easy
DISADVANTAGES;
1. Initial cost is high
2. High maintenance cost.

APPLICATION;
1. This unit assists with FMS (FLEXIBLE MANUFACTURING
SYSTEM)
2. It helps in un manned industry .
3. Industrial Application
4. Medium scale automation industries

CIRCUIT DETAILS
1. Micro controller system
2. Interface Circuit for solenoid valves
3. Power supply (230V A.C. to 12 V and 5V DC)
4. Key Board Circuit
MICRO CONTROLLER SYSTEM:
This system monitors the engine condition by using PIC 16F870 (28
pin IC Package) micro controller. The pin details of micro controller are
shown in figure.
The circuit diagram for this micro controller board is shown below,

MOTHER BOARD CIRCUIT DETAILS
in no 2&5.The pin no 1 is RESET switch..The INPUTS are connected to
port B .The OUTPUTS are connected to PORT C.6 MHZ crystal is
connected to pin no 9,10.

POWER SUPPLY 5V DC AND 12V DC;
A 12 –0 v step down transformer is used to stepdown 230V AC to
12V AC .This 12V AC supply is converted to 12V DC using four rectifier
diodes. The voltage from the rectifier section is regulated to 12V DC using
7812 IC . From 12V DC the 7805 IC is used for regulating 5V DC for the
power supply of microcontroller.the power supply circuit is shown in fig.

INTRODUCTION:
All the electronic components starting from diode to Intel IC’s only
work with a DC supply ranging from +5V to +12V. We are utilizing for the
same, the cheapest and commonly available energy source of 230V-50Hz
and stepping down, rectifying, filtering and regulating the voltage.
STEP DOWN TRANSFORMER:
When AC is applied to the primary winding of the power transformer,
it can either be stepped down or stepped up depending on the value of DC
needed. In our circuit the transformer of 230V/15-0-15V is used to perform
the step down operation where a 230V AC appears as 15V AC across the
secondary winding. Apart from stepping down voltages, it gives isolation
between the power source and power supply circuitries.
RECTIFIER UNIT:
In the power supply unit, rectification is normally achieved using a
solid state diode. Diode has the property that will let the electron flow easily
in one direction at proper biasing condition. As AC is applied to the diode,
electrons only flow when the anode and cathode is negative. Reversing the
polarity of voltage will not permit electron flow. A commonly used circuit
for supplying large amounts of DCpower is the bridge rectifier. A bridge
rectifier of four diodes (4 x IN4007) are used to achieve full wave
rectification. Two diodes will conduct during the negative cycle and the
other two will conduct during the positive half cycle, and only one diode
conducts. At the same time one of the other two diodes conducts for the
negative voltage that is applied from the bottom winding due to the forward
bias for that diode. In this circuit due to positive half cycle D1 & D2 will
conduct to give 0.8V pulsating DC. The DC output has a ripple frequency

of 100Hz. Since each alteration produces a resulting output pulse, frequency
= 2 x 50 Hz. The output obtained is not a pure DC and therefore filtration
has to be done.
The DC voltage appearing across the output terminals of the bridge
rectifier will be somewhat less than 90% of the applied rms value. Normally
one alteration of the input voltage will reverse the polarities. Opposite ends
of the transformer will therefore always be 180 degree out of phase with
each other. For a positive cycle, two diodes are connected to the positive
voltage at the top winding.
FILTERING CIRCUIT:
Filter circuits which is usually capacitor acting as a surge arrester
always follow the rectifier unit. This capacitor is also called as a decoupling
capacitor or a bypassing capacitor, is used not only to ‘short’ the ripple with
frequency of 120Hz to ground but also to leave the frequency of the DC to
appear at the output. A load resistor R1 is connected so that a reference to
the ground is maintained. C1, R1 is for bypassing ripples. C2, R2 is used as
a low pass filter, i.e. it passes only low frequency signals and bypasses high
frequency signals. The load resistor should be 1% to 2.5% of the load.
1000mf/25V : for the reduction of ripples from the pulsating
10mf/25V : for maintaining the stability of the voltage at the load side.
0.1mf : for bypassing the high frequency disturbances

BLOCK DIAGRAM FOR POWER SUPPLY
STEP DOWN BRIDGE POSITIVE
TRANSFORMER RECTIFIER CHARGE
CAPACITOR
5V 12V
REGULATOR REGULATOR
MOTHER DISPLAY
BOARD BOARD RELAY

5 TO 12 V DC DRIVE CARD
Here we have to drive the 12V DC load. The 5V signal from the PIC
16F870 micro-controller is fed into the input of interface circuit. SL100
transistor is used here for high speed switching purpose and IRF 540N
MOSFET is connected to the motor to handle the larger current drawn by the
solenoid valve.

DESCRIPTION OF
PNEUMATIC
COMPONENTS

INTRODUCTION TO PNEUMATICS
In engineering field may Machines make use of a fluid or compressed air to
develop a force to move or hold an object A system which is operated by
compressed air is known as Pneumatic System. It is most widely used the
work Piece turning drilling sawing etc.
By the use of Pneumatic System the risk of explosion on fire with
compressed air is minimum high working speed and simple in construction.
PNEUMATIC COMPONENTS
In engineering field, many machines make use of fluid for developing
a force to move or hold an object. A number of fluid can be used in devices
and system. Two commonly used fluids are oil and compressed air. A
system which is operated by compressed air. A system which is operated by
compressed air is know as pneumatic system.
Discrete Control Logic
1. Pneumatic circuits - Low forces
- Discrete, fixed travel distances
- Rotational or reciprocating motion
Main components: compressor, valves, cylinders
AIR COMPRESSOR

Compressor is a device which gets air fro the atmosphere and
compresses it for increasing the pressure of air. Thus the compressed air.
Thus the compressed air used for many application.
The compression process requires work in put. Hence a compressor is
driven by a prime mover. Generally an electric motor is used as prime
mover. The compressed air from compressor is stored in vessel called
reservoir. Fro reservoir it be conveyed to the desired place through pipe
lines.
2. FLTER
In pneumatic system, an air filter is used to remove all foreign matter.
An air filter dry clean air to flow without resistance various materials are
used for the filter element. The air may be passed thorugh a piece metal, a
pours stone felt resin impregnated paper. In some filters centrifugal action
or cyclone action is used to remove foreign matters.
3. PRESSURE REGULATOR

Constant pressure level is required for the trouble free operation of a
pneumatic control., A pressure regulator is fitted downstream of the
compressed air filter. It provides a constant set pressure at the outlet of the
outlet of the regulator. The pressure regulator is also called as pressure
reducing valve or pressure regulating valve.
4. LUBRICATOR
The purpose of an air lubricator is to provide the pneumatic
components with sufficient lubricant. These lubricants must reduce the wear
of the moving parts reduce frictional forces and protect the equipment from
corrosion.
Care should be taken to ensure that sufficient lubrication is provided.
But excessive lubrication should be avoided. .
5. FLR Package (or) FRL Package
The air service unit is a combination of following units.
1. Compressed air filter
2. Compressed air regulator
3. Compressed air lubricator
Air Filter, regulator and lubricator are connected together with close
nipples as one package. This unit is know as FLR (Filter, regulator,
lubricator.)
6. PRESSURE CONTROL VALVE :

Each hydraulic system is used to operate in a certain pressure range.
Higher pressure causes damage of components. To avoid this pressure
control valves are fitted in the circuits.
7. Direction control valve :
Directional control valves are used to control the direction of flow.
The design principle is a major factor with regard to service life actuating
force switching times etc.
8. Piston and Cylinder
single acting pneumatic cylinder;
PNEUMATIC CITCUIT SYMBOL FOR SINGLE ACTING PNEUMATIC
CYLINDER;

Pneumatic cylinders (sometimes known as air cylinders) are mechanical
devices which produce force, often in combination with movement, and are
powered by compressed gas (typically air).
To perform their function, pneumatic cylinders impart a force by converting
the potential energy of compressed gas into kinetic energy. This is achieved
by the compressed gas being able to expand, without external energy input,
which itself occurs due to the pressure gradient established by the
compressed gas being at a greater pressure than the atmospheric pressure.
This air expansion forces a piston to move in the desired direction. The
piston is a disc or cylinder, and the piston rod transfers the force it develops
to the object to be moved.
When selecting a pneumatic cylinder, you must pay attention to:
· how far the piston extends when activated, known as "stroke"
· surface area of the piston face, known as "bore size"
· action type
· pressure rating, such as "50 PSI"
· type of connection to each port, such as "1/4" NPT"
· must be rated for compressed air use
· mounting method
Types
Single acting cylinders
Single acting cylinders (SAC) use the pressure imparted by compressed air
to create a driving force in one direction (usually out), and a spring to return
to the "home" position

Double acting cylinders
Double Acting Cylinders (DAC) use the force of air to move in both extend
and retract strokes. They have two ports to allow air in, one for outstroke
and one for instroke.
Although pneumatic cylinders will vary in appearance, size and function,
they generally fall into one of the specific categories shown below. However
there are also numerous other types of pneumatic cylinder available, many
of which are designed to fulfill specific and specialised functions.
Other types
Although SACs and DACs are the most common types of pneumatic
cylinder, the following types are not particularly rare:
· Rotary air cylinders: actuators that use air to impart a rotary motion

· Rodless air cylinders: These have no piston rod. They are actuators
that use a mechanical or magnetic coupling to impart force, typically
to a table or other body that moves along the length of the cylinder
body, but does not extend beyond it.
Sizes
Air cylinders are available in a variety of sizes and can typically range from
a small 2.5 mm air cylinder, which might be used for picking up a small
transistor or other electronic component, to 400 mm diameter air cylinders
which would impart enough force to lift a car. Some pneumatic cylinders
reach 1000 mm in diameter, and are used in place of hydraulic cylinders for
special circumstances where leaking hydraulic oil could impose an extreme
hazard.
Pressure, radius, area and force relationships
Although the diameter of the piston and the force exerted by a cylinder are
related, they are not directly proportional to one another. Additionally, the
typical mathematical relationship between the two assumes that the air
supply does not become saturated. Due to the effective cross sectional area
reduced by the area of the piston rod, the instroke force is less than the
outstroke force when both are powered pneumatically and by same supply of
compressed gas.
The relationship, between force on outstroke, pressure and radius, is as
follows:

This is derived from the relationship, between force, pressure and effective
cross-sectional area, which is:
F = p A,
With the same symbolic notation of variables as above, but also A represents
the effective cross sectional area.
On instroke, the same relationship between force exerted, pressure and
effective cross sectional area applies as discussed above for outstroke.
However, since the cross sectional area is less than the piston area the
relationship between force, pressure and radius is different. The calculation
isn't more complicated though, since the effective cross sectional area is
merely that of the piston less that of the piston rod.
For instroke, therefore, the relationship between force exerted, pressure,
radius of the piston, and radius of the piston rod, is as follows:
Where:
F represents the force exerted
r1 represents the radius of the piston
r2 represents the radius of the piston rod
π is pi, approximately equal to 3.14159.

VALVE CONNECTORS;
POLYURETHANE TUBE ; shortly say PUN tube;
Manual operations involving heavy lifting. Pushing or pulling
motions can be firing for the operations and can induce a monotony which
results in lowered production. Cylinders have been designed to carry out
these movements with a pre – determined force and stroke and can be fitted
to synchronize with operation cycles of many machines it is worth wile to
examine the existing plan and methods of movement and to consider the
numberous mechanical applications which the range of pneumatic cylinders
make possible. Quality is to keynote of air cylinder. Engineer them into
you production setup to get the last ounce of power, speed and efficiency to
save time, space and money.

Piston is cylinder part which moves in a cylinder have corresponding
hole on it. To make the strokes effective there is no gap between them or
with a very tiny gap, part of the micron. The cylinder and its piston have a
glazing surface where there is a contact between them for easy motion of
piston and avoiding wear and tear of both. The outer side of the cylinder
have mountings consists of plate and studs attached with it. But the of these
mountings, the cylinder and piston assembly can fitted on any place of the
piston have threads on it for fastening the other parts (or) accessories
according the operating performed and the application required. We can fit
holding devices, Clamping materials or other metal cutting and forming
ports with which can be movable with the piston.
Pneumatics are used practically in every industry for a wide variety of
manufacturing process, pneumatics equipments are used for multiple
reasons. The best reason is that it is air powered ordinary air turns out to be
very excellent as a fluid power components.
Solenoid Valve :
In order to automate the air flow in our system we have to provide an
electrically controlled valves. Electrical devices can provide more effective
control, less expensive interlocks having many additional safety features and
simplified automatic sequencing when a machine must operate in a
hazardous area, remote actuation is a desirable. The operator can provide

satisfactory control though electrical devices from a remote point with in a
safe area, uding a semi automatic system and these electrical flow control
devices are also in use in full automation by providing proper action signals.
Push and pull actuation can be priced b solenoids. These movements
are used to open and close the pop pet type valves. These actuations are
done according to the signals given to the solenoid coil when the decided by
the program. The outlet of solenoid coil when the decided by the program,.
The outlet of solenoid valve is connected to a spray gun, which is going to
spray the paint.
SOLENOID OPERATED VALVES:
Solenoid valves are electromechanical devices like relays and
contractors. A solenoid valve is used to obtain mechanical movement in
machinery by utilizing fluid or air pressure. The fluid or air pressure is
applied to the cylinder piston through a valve operated by a cylindrical
electrical coil. The electrical coil along with its frame and plunger is known
as the solenoid and the assembly of solenoid and mechanical valve is known
as solenoid valve. The solenoid valve is thus another important
electromechanical device used in control of machines. Solenoid valves are
of two types,
1. Single solenoid spring return operating valve,(5/2)
2. Double solenoid operating valve.
In fig 1 is shown a single solenoid spring return valve in its de-energized
condition. The symbol for the solenoid and the return are also shown. The
solenoid valve is shown connected to the cylinder to help readers understand
the solenoid valve action. In the de energized condition, the plunger and the
valve spool position as shown in figure 1.

In this position of spool, port P is connected to port A and port B is
connected to tank or exhaust (i.e. atmosphere) if air is used. Spring pressure
(S) keeps the spool in this condition as long as the coil is de energized.
Fluid pressure from port P through port A is applied to the left side of the
cylinder piston. Thus the cylinder piston moves in the right direction. Now
when the solenoid coil is energized, plunger is attracted and it pushes the
spool against spring pressure.
The new position of plunger and spool are shown in fig 2.
In this position of spool, port A gets connected to tank and port P gets
connected to port B. Thus pressure is applied to the cylinder piston from

right and moves the piston rod to the left. At the same time fluid in the other
side is drained out to the tank. When the solenoid coil is again de energized,
the spring (S) will move the spool to its original position as shown in figure
1. Thus, normally when the solenoid coil is de energized the piston rod
remains extended.
PNEUMATIC FITTINGS:
There are no nuts to tighten the tube to the fittings as in the
conventional type of metallic fittings. The tube is connected to the fitting by
a simple push ensuring leak proof connection and can be released by
pressing the cap and does not require any special tooling like spanner to
connect (or) disconnect the tube from the fitting.

SPECIFICATION OF THE FITTING:
Body Material - Plastic
Collect/Thread Nipple - Brass
Seal - Nitrate Rubber
Fluid Used - Air
Max. Operating Pressure - 7 Bar
Tolerance on OD of the tubes - ± 1 mm
Min. Wall thickness of tubes - 1 mm.
FLEXIBLE HOSES:
The Pneumatic hoses, which is used when pneumatic components
such as actuators are subjected to movement. Hose is fabricated in layer of
Elastomer or synthetic rubber, which permits operation at high pressure.
The standard outside diameter of tubing is 1/16 inch. If the hose is subjected
to rubbing, it should be encased in a protective sleeve.

ADVANTAGES AND LIMITATIONS
ADVANTAGES:
¨ The Pneumatic arm is more efficient in the technical field
¨ Quick response is achived
¨ Simple in constructions
¨ Easy to maintain and repair
¨ Cost of the unit is less when compared to other robotics
¨ No fire hazrd problem due to over loading
¨ Comparatively the operation cost is less
¨ The operation of arm is faster because the media to operate is air
¨ Continuous operation is possible without stopping.
LIMITATIONS:
¨ High torque cannot be obtained.
¨ Load Carrying capacity of this unit is not very high (3 – 5 kgs).
While working, the compressed air produces noise, therefore a silencer may be
used.

COST ESTIMATION
DETAILS COST Rs.
1. Double acting cylinder25MM DIA X100 MM Lengthx 1nos
2. camera
3. 5/2 way solenoid operated directional control valve-1no
4. Flow control valves1 no
5. M.S. square angle fabricated stand 300W x 300 Bx 600H
6. Polyurethane tube 6meters
7. Valve connectors 5 nos
8. Conveyor belt assembly unit
9. Microcontroller unit
10.DC motor 24VDC
TOTAL
500
600
500
400
800
200
200
1000
1200
600
-------------
6000/-

CONCLUSION
We make this project entirely different from other projects. Since
concepts involved in our project is entirely different that a single unit is used
to various purposes, which is not developed by any of other team members.
By doing this project we gained the knowledge of pneumatic system
and how automation can be effectively done with the help of pneumatic
system.
It is concluded that any automation system can be done with the help
of controller& pneumatic system.
We have successfully completed the project work on using pneumatic
control at our Institute.
By doing this project work, we understood the working principle and
uses of various controls, switches, relays etc.
It will be of no doubt that pneumatic system will be an integrated part
of any automation process in any industry.
Once again we express our sincere thanks to our staff members.

BIBLIOGRAPHY
· Low cost automation with pneumatics - FESTO
· Electro pneumatics - FESTO
· www.google.com
· WORKSHOP : W.J. CHAPMAN
· PRODUCTION TECHNOLOGY : R.K. JAIN
· PRODUCTION TECHNOLOGY : R.K. JAIN & S.C. QUPTA
· METAL FORMING PROCESS : R.S. KURMI
· MANUFACTURING PROCESS : K. RAMACHANDRAN
· MACHINE SHOP TECHNOLOGY : S.S. MANIAN &
 RAJAGOPAL &
 G. BALAJI
SINGH
· DESIGN OF MACHINE ELEMENTS : R.S. KURMI &
 P.N.
VENKATESAN
· DESIGN OF MACHINE ELEMENTS : RAMACHANDRAN
· DESIGN DATA BOOK : P.S.G. COLLEGE OF
 TECHNOLOGY

Machine vision amk mtmr final

More Related Content

What's hot

Similar to Machine vision amk mtmr final

More from chockalingam athilingam

Recently uploaded

Machine vision amk mtmr final