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Applying Automated
Optical Inspection
by Ben Dawson, Ph.D., DALSA Coreco, ipd Group
The goal is to develop an accurate, fast AOI system as flexible and easy to
train as a human.
Automated optical inspection (AOI) uses lighting, cameras, and vision computers to make precise, repeatable, high-speed evaluations
of a wide range of products. Human vision has limited accuracy and is slow but
very flexible and easy to train. Mechanical gauging is accurate and precise but
slow and cannot be used to evaluate changes in visual appearance.
A machine vision or AOI system can take millions of data points (pixels) in a
fraction of a second. These data points are used for visual inspection and
precision measurement. With modest effort and cost, an AOI system can resolve
about 25 microns. With increasing effort and cost, measurement resolution can
approach a micron.
Typical applications for AOI include the following:
• Gauging the diameters and concentricity of holes in automotive parts.
• Ensuring that lids and labels are properly applied to food and pharmaceutical
products.
• Evaluating molded parts against three-dimensional (3-D) CAD data.
• Ensuring that all parts are present in a product assembly.
• Checking for cracks, flaws, contamination, scratches, and other defects.
• Optical character recognition (OCR).
• Grading agricultural products such as seed corn or fruit.
From these applications, you see that AOI systems are used for inspecting parts
that have limited and known variations. For defect or flaw detection, the AOI
system looks for differences from a perfect part. Agricultural inspections might
check for variations in part color, perhaps to find ripe fruit. To successfully
apply AOI, you need to set up the AOI system for specific types of parts and
limit the visual appearance of those parts.
An AOI system also must be set up or trained to inspect visual features of the
parts. For example, you must tell the AOI system what features to measure on an
automotive part or teach it the color of ripe fruit for sorting agricultural
products. We are working on making setup and training easier, but current
technology is nowhere near a human’s ability to understand and quickly learn
what parts and features to inspect.
What Is in an AOI System?
Figure 1 is a schematic diagram of a typical AOI system. This particular system
inspects automotive bearings for cracks and flaws, but the components and
methods are similar for other AOI applications.
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Figure 1. Schematic Diagram of an AOI System |
In this example, bearings are released by a feed cogwheel and slide down an
inclined track. The track has rails that limit the part’s side-to-side movement.
This kind of mechanical restraint is known as staging or fixturing. Staging
positions the part in a known location and decreases variability in where the
parts are and how they look. This reduces the computation required by the vision
computer so that parts are quickly inspected.
As the bearing slides down the track, it interrupts a laser beam. A
part-in-place (PiP) sensor detects this interruption and signals the vision
computer that the part is in a known location. The vision computer then triggers
the five cameras to simultaneously acquire images of the bearing. When a PiP
sensor cannot be used to trigger image acquisitions, the vision computer must
detect when a part is present by analyzing the images, and this slows down the
system.
Lighting the part is critical for AOI. Obviously, the AOI system must be able to
see the parts and features to do the inspection. Beyond this, lighting amplifies
features of interest and suppresses visual features that are noise.
For example, many products reflect the light sources, causing bright highlights
in the image. Highlights can obscure features in the image that we want to
inspect. In this example, we use a large, diffused red LED light directly above
the part. The cameras are set at an angle so they can see both the top and sides
of the part, but there is no highlight. This allows the visualization and
detection of fine cracks in the part as well as chip-outs along the top edge.
Staging and lighting are critical for an AOI system because they reduce
variability in part images and act as preprocessors to select image data for the
vision computer. Without this preprocessing, the vision computer would be too
slow or unable to do the inspection.
You may be able to use an AOI system that has built-in staging and lighting, but
often these have to be designed for your AOI task. A variety of standard lights
and mechanical components helps with this task.
 The camera’s lens forms an image of the bearing on the camera’s sensor,
typically a CCD or CMOS image array sensor. Inexpensive machine vision quality
lenses are used in this inspection, but inspecting small parts or high precision
and accuracy measurements requires more expensive lenses. Again, the optics may
be included in the AOI system or chosen for your specific task.
The camera translates the pattern of light from the part into an electronic
image. Cameras designed for AOI systems have square (1:1 aspect ratio) pixels to
simplify measurements, progressive scanning rather than interlaced scanning, a
fast shutter, and an asynchronous trigger for acquiring images.
The progressive scanning and fast shutter reduce blurring of the part’s image
due to movement of the part. The trigger is necessary to synchronize the image
acquisition with the presence of the part.
The brains of an AOI system are a vision computer and its software. This
computer analyzes the images to extract measurements, counts, colors, and other
visual features needed to do the inspection. The results of the inspection are
used to reject defective parts.
In this example, a compressed air kicker is activated to remove defective
bearings from the line. The vision computer also sends statistics and process
data to a database.
Another Example: Grading Corn
The AOI task in Figure 2 is to find the ratio of bad (dark) corn kernels to the
total of good (yellow and orange) and bad kernels. This ratio is used to grade
seed corn lots; lots with fewer bad kernels sell for more.
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Figure 2. Image of Corn Kernel Inspection |
A typical AOI measurement task assumes ridged, well-defined parts and that any
variation beyond some tolerance is a defect. Here, the parts are not well
defined in size and shape, so trying to use a caliper tool to measure kernel
size would be useless and frustrating. Instead, we use the known colors of good
and bad kernels to approximate the desired ratio.
The staging and lighting consist of the operator taking a scoop of corn from a
lot and spreading the kernels on a light table so the kernels are not
overlapping. Since this is done on a sample evaluation basis, automating the
staging is not worth the cost and effort.
The ratio measurement must be consistent over time and across operators. The
evaluation task must apply objective standards to classify bad and good corn
kernels.
We teach the AOI system the color and color variation of known good and bad corn
kernels and the color of the background. The ratio we want is approximated well
enough by taking the ratio of pixels with bad colors to the pixels with bad or
good colors, ignoring the background color. While not as exact as if we had
counted each kernel, it is a lot faster and removes the operator’s subjective
judgment from the evaluation.
Advances in Applying AOI
Many of the problems in applying AOI arise from the limited intelligence and
flexibility of an AOI system. We can pick up a part, examine it with various
views and lighting, do a lot of neural processing, and draw conclusions based on
our knowledge about objects and the materials they are made from. An AOI system
has to rely on staging to present the part and has a limited time to examine the
part. It doesn’t understand objects and materials and has very limited
processing capabilities.
Improvements in lighting, computing capability, and vision software have made
AOI systems smarter and more flexible, though still far from human visual
intelligence.
Lighting preprocesses the image to amplify features you want to in-spect and
suppress noise. Advances in lighting have improved the capabilities of vision
systems, in part by reducing the computation required by the vision computer.
The adoption of standard LED-based lighting has improved AOI systems because it
is very stable and easily controlled when compared to the older incandescent and
fluorescent lighting solutions. For example, we can strobe an LED light source
to give a brief and intense flash of light that stops part motion. This is
difficult to do with older lighting technology.
Another lighting method projects a pattern of light on an object, often by using
a laser with a holographic lens. The distortions of this structured light
pattern can be measured and processed to recover the object’s 3-D structure, at
least what we can see of it. AOI systems using structured light can, for
example, compare complex objects such as engine blocks to the designed shape in
CAD files.
Another major boost to the intelligence of AOI systems comes from the rapid
improvement in PCs. AOI tasks that previously required special computing
hardware now are done with generic PCs along with hardware for image
acquisition, communications, and synchronization. Demanding inspection tasks,
such as inspecting LCD flat-panel screens, still require the horsepower of a
dedicated vision processor.
The biggest advance in applying AOI is the improvement in the vision computer’s
software. In the not-so-good old days, you could expect to spend many months
laboriously programming the vision computer for your task. The thrust of recent
software development makes this task much easier by providing interfaces to hide
the hardware details and incorporating the specialized knowledge needed to do
AOI tasks.
The mantras of AOI vision computer vendors currently are ease of setup and ease
of use. With a specialized AOI system, perhaps for 3-D measurement using
structured light, the setup and operator interfaces can be very easy to use
because the task domain is very limited and well specified.
If you need a custom AOI system, then you, an integrator, or a vision component
vendor have to write the AOI software. Rapid application development (RAD)
packages, such as ipd’s Sherlock™, make this relatively easy. These packages
typically have an easy-to-use interface with features such as drag-and-drop
selection of tools and operations and online help.
If you need extra computing power or find the RAD package limiting, there are
many mature software libraries. Just be prepared for a long learning curve.
Many AOI tasks can be solved with a good set of general vision tools. These
tools include visual search, measurement, defect detection, and bar-code and OCR
reading.
Vision computer vendors have developed packages that bundle these tools inside a
graphical user interface. No programming is required, and most of the
specialized knowledge needed to solve an AOI task is incorporated in the
software.
Summary
AOI has many applications but is limited to well-specified parts in
well-controlled settings. It would be nice to have an AOI system as flexible and
as easy to train as a human but with the speed, accuracy, and resolution of a
computer vision system. Such systems are many years off, but that doesn’t
discourage us from continually improving existing AOI systems.
The three major efforts in putting together an AOI system are building the part
staging, getting the right lighting, and programming the vision computer.
Improvements and standardization of lighting and mechanical fixtures have made
the first two tasks much easier. The improvement in computing power and vision
software, particularly the focus on easy-to-setup and easy-to-use vision
software, continues to make it simpler to program the vision computer.
Developing a fully custom vision system using traditional software libraries can
take many months of work. Using a RAD package reduces the time to weeks.
For many common AOI applications, new programming-free software packages can cut
development time to a few days. In all cases, get help with staging, lighting,
optics, and the camera choice.
About the Author
Ben Dawson, Ph.D., is director of strategic development at DALSA Coreco, ipd
Group. He earned M.S.E.E. and Ph.D. degrees from Stanford University and also
was on the staff of M.I.T. Dr. Dawson has written more than 50 scientific and
technical papers on human and machine vision. DALSA Coreco, ipd Group, 900
Middlesex Turnpike, Building 8, Second Floor, Billerica, MA 01821-3929,
978-670-2050, e-mail: bdawson@goipd.com
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