Transcript AUTOMATION & ROBOTICS LECTURE#03 AUTOMATION BUILDING BLOCKS By: Engr. Irfan Ahmed Halepoto Assistant Professor.

AUTOMATION & ROBOTICS
LECTURE#03
AUTOMATION BUILDING BLOCKS
By: Engr. Irfan Ahmed Halepoto
Assistant Professor
AUTOMATION
• Use of control systems and information
technologies to control industrial machinery and
processes,
• Reducing the need for human involvement in the
production of goods and services.
AUTOMATION BUILDING BLOCKS
• Sensors
• Analyzers
• Actuators
• Drives
Drives
Vision systems
• Vision systems
– (Machine/Computer)
Actuators
Sensors
Analyzers
SENSORs
• A sensor is a device which receives and responds
to a signal.
• Sensor measures a physical quantity and converts
it into a signal which can be read by an observer
or by an instrument.
– Thermocouple converts temperature to an
output voltage which can be read by a
voltmeter.
Sensor Category wise……..
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Temperature sensors
Light Sensors
Force Sensors
Pressure Sensors
Displacement Sensors
Motion Sensors
Sound Sensors
Sensors operation wise ……
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Resistive
Inductive
Capacitive
Piezoelectric
Photoresistive
Elastic
Thermal
Building Blocks: sensor Versality
Sensors variety
Inductive proximity sensors
Absolute rotary
encoder
Linear encoder
Building Blocks: sensor devices
Building Blocks – Sensor features
• Range: normal range (maximum & minimum values)
over which the controlled variable might vary?
• Resolution or discrimination: smallest discernible
change in the measured value
• Response time: Amount of time required for a
sensor to completely respond to a change in its
input.
• Error: difference between the measured and actual
values.
• random errors & systematic errors
• Accuracy: How close the sensor comes to indicating
the actual value of the measured variable?
Building Blocks – sensor features
• Precision: How consistent the sensor is in
measuring the same value under the same
operating conditions over a period of time?
• Sensitivity: How small a change in the controlled
variable the sensor can measure?
• Linearity: Exaggerated relationship between the
ideal and the actual measured or calibration line
• Dead band: How much of a change to the process
is required before the sensor responds to the
change?
• Costs: What are the costs involved - not simply the
purchase cost, but also the installed/operating
costs?
ANALYZERS
• A device that analyzes given data.
• It examines in detail the structure of the given data
and tries to find patterns and relationships between
parts of the data.
• Through analyzers, various parameters like rotation,
speed, position, angles etc can be analyzed in a
specific process control.
• Analyzer can be a piece of hardware or a software
program running on a computer.
– Process Analyzer
– Bar Code Analyzer
– Logic Analyzer
– Spectrum Analyzer
– Encoders
Process Analyzers
Barcode Analyzers
Barcode Analyzers
Barcode Patterns
Building Blocks: Analyzers
Encoder example: An absolute optical encoder has 8
rings, 8 LED sensors, and 8 bit resolution. If the output
pattern is 10010110, what is the shaft’s angular position?
Ring
Angle
(deg)
Pattern
Value
(deg)
1
180
1
180
2
90
0
3
45
0
4
22.5
1
5
11.25
0
6
5.625
1
5.625
7
2.8125
1
2.8125
8
1.40625
0
Total
22.5
210.94
ACTUATORS
• Actuators are a subdivision of transducers
• Actuators transform an input signal (mainly an
electrical signal) into motion.
• It is operated by a source of energy, usually in the
form of an electric current, hydraulic fluid pressure
or pneumatic pressure, and converts that energy
into some kind of motion.
• Actuators are used as mechanisms to introduce
motion, or to clamp (fix) an object so as to prevent
motion.
Actuator Examples….
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Electrical motors,
Pneumatic actuators,
Hydraulic actuators,
Linear actuators,
Comb drive (capacitive actuators)
Piezoelectric actuators
Actuator Examples….
Hydraulic actuators
Pneumatic actuators
Piezoelectric actuators
Comb-drives
• Comb-drives (capacitive actuators) are linear motors that
utilize electrostatic forces that act between two metal
combs.
• Comb-drives based actuators are specifically suited for
large displacement application.
• Almost all comb-drives are built on the micro or nano-scale
and are typically manufactured using silicon.
DRIVES
• Driver is responsible to control (run the process)
the specific state of any particular type of device
that is attached to any system (i.e. motor,
computer).
• Drives commonly operate and control the direction
and speed of a specific device.
Building Blocks: Drives Versality
• AC/DC servomotors
•Stepper Motors
• Induction Motors
• Kinematic devices
• Digital drives
•Hard Drives
Building Blocks: Drives
AC motors
DC motors
Digital Drives
Stepper motors
AC motors
• Motor’s name comes from
the alternating current (ac)
“induced” into the rotor by
the rotating magnetic flux
produced in the stator.
• Motor torque is developed
from interaction of currents
flowing in the rotor bars and
the stator’s rotating magnetic
field.
AC motors
Stator
• Stator structure is composed of steel laminations shaped to
form poles around which are wound copper wire coils.
• These primary windings connect to, and are energized by,
the voltage source to produce a rotating magnetic field.
• Three-phase windings spaced 120 electrical degrees apart
are popular in industry.
Rotor
• Rotor is another assembly of laminations over a steel shaft
core.
• Radial slots around the laminations’ periphery house rotor
bars—cast-aluminum or copper conductors shorted at one
end and positioned parallel to the shaft
Industrial AC Induction Motors
• Industrial AC induction motors
are designed to operate with
a current that alternates in the
direction of flow 60 times per
second (HZ).
• If this frequency of alternation
is changed, the speed of the
motor changes.
• By
controlling
the
AC
frequency with a variable
frequency drive, you control
speed.
Building Blocks: digital drives
• Microprocessors & DSP’s are replacing analog components
with digital components (i.e., digital drives).
• Need for A/D and D/A interfaces is rapidly declining, being
replaced by a high- speed network between the master host
(a PC) and the distributed digital slave devices.
servowire implementation of IEEE 1394
PWM and digital drives (binary control!)
• PWM (Pulse Width Modulation)- a constant frequency, twovalued signal (e.g., voltage) in which the proportion of the
period for which the signal is on and the period for which it
is off can be varied.
• Percentage of time on is called the duty cycle.
• Voltage value will depend on the application.
• PWM frequency must be high enough so that motor cannot
respond to a single PWM signal
On
25% duty cycle
Off
On
50% duty cycle
Off
T
2T 3T 4T
T
2T 3T 4T
MACHINE VISION
• Machine vision (MV) or computer vision is the process
of applying a range of technologies to provide imagingbased automatic inspection, process control and robot
guidance in industrial applications.
• Machine vision is the capturing of an image (a snapshot
in time), the conversion of the image to digital
information, and the application of processing
algorithms to extract useful information about the image
for the purposes of pattern recognition, part inspection,
or part positioning and orientation.
• The main categories into which MV applications fall are
quality assurance, sorting, material handling, robot
guidance, and calibration.
Building Blocks – machine vision
Algorithm
PC
Machine Vision
Equipment:
• Computer
• Frame grabber
Types:
• Camera (CCD array)
Front
• Lenses
Back
• Lighting
Side
• Calibration templates
Structured
• Algorithms
Strobe
Machine Vision: structured lighting
• Structured Lighting is used in a
front
lighting
mode
for
applications requiring surface
feature extraction.
• Structured lighting is defined
as the projection of a crisp line
of light onto an object.
• The patterned light is then
used to determine the 3-D
characteristics of an object
from the resulting deflections
observed.
Note the non-typical approach of
projecting a grid array of light on an
object to detect features
Machine Vision: Image processing
• Segmentation: Define and separate regions of
interest.
• Thresholding: Convert each pixel into binary (B
or W) value by comparing bit intensities.
• Edge detection: Locate boundaries between
objects
• Feature extraction: Determine features based on
area and boundary characteristics of image.
• Pattern recognition: Identify objects in midst of
other objects by comparing to predefined models
or standard values (of area, etc.)