Machine-vision systems – what can they do for you? – Free Online Library Essay

Machine-vision systems– what can they do for you?



Machine-vision (MV) systems can be applied to many manufacturing
operations; where human vision now is required. These systems are best
for applications in which their speed and accuracy over long periods of
time enable them to outperform humans.

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But what about your plant? Can you cash in on the promised
benefits? To answer these questions, let’s examine MV systems to
determine where they can be applied and the types of problems they can
eliminate.



The most important use of machine vision is precluding defects
during a manufacturing operation. Consequently, cost justification for
an MV system stems from savings attributed to the cost of poor quality.



A recent Booz Allen ; Hamilton study emphasized there are two
elements to the cost of quality–the cost of control and the cost of
failure. The essence of the study is that we must include the savings
from both in any justification equation for systems that improve
quality.



The cost of control is fairly easy to identify and quantify. It
includes the cost of preventing and finding defects before products are
shipped. The most obvious costs are the labor used in QC activities,
and the investment in inspection equipment. These might be large or
small, depending on the size and complexity of your operation.



The costs of failure are much more difficult to quantify. They
include the internal cost resulting from material scrap and rework, and
the external cost stemming from warranty claims, liability actions, and
recall orders, as well as hidden costs, such as loss of customers.


Successful applications



Properly applied, machine-vision systems can be a primary means of
avoiding both internal and external failures. For example, human-based
inspection techniques normally are structured to detect out-of-tolerance
parts after they are produced. MV can spot trends–precursors of
producing scrap–while parts are still within tolerance.



Moreover, laser gages and linear-array sensors can measure part
dimensions during, or immediately after, a machining operation. This
data then serves as a guide for adjusting a machine tool or replacing
cutting tools before generating scrap.



The automotive industry is moving quickly to adopt these
statistical processcontrol techniques. One example is Chrysler
Corp’s Windsor, Ont, Minivan assembly plant, where a Perceptron system is inspecting every door/hinge assembly (see box
“Assembly-line QC by MV’). Trend analysis and frequency
distributions developed from data collected by sensors in the system
report changes in production quality. This ability to track production
data and take corrective action often requires applying MV to inspect
every part.



A side benefit–without regard to the amount of in-process data
available from other monitoring methods–is reduced paperwork because
recordkeeping is automated. Also, MV systems have the ability to
transfer data between themselves and process controllers, or even a
master computer.



In some manufacturing operations it’s impossible to completely
eliminate defects, even with a machine-vision system. In such cases, MV
can be of value in separating good parts from scrap. Or, it can
separate defective assemblies into two groups–those that can be
reworked and those that can’t. One example of the latter is found
in the electronics industry, Figure 1.



Industry experts estimate that a faulty printed-circuit board
detected immediately after fabrication can be repaired for as little as
25^. Once the same board is fully loaded with components, rework cost
jumps to about $40. The cost to locate and repair the defect becomes
even higher after the board is installed in a control system.



Similarly, with most parts or assemblies, the cost to scrap or
rework a defective unit spirals up with each value-added step in the
manufacturing process. Therefore, the sooner you catch and correct an
out-of-tolerance condition, the better off you will be.


In the case of machined surfaces, parts with dimensions exceeding
the maximum size tolerance usually can be reworked. On the other hand,
undersize parts are very difficult, sometimes even impossible, to
salvage. Vision systems can be used to make the distinction between
reworkable parts and scrap.



Save on tooling



MV systems can be valuable in applications where expensive hard
tooling is required to hold a part during a machining, forming, welding,
or similar operation. Many times such tooling can be eliminated, or
replaced by less expensive, flexible tooling in conjunction with a
machine-vision system.



For example, an Automatix Partracker is used at a Norfolk and
Western repair depot in welding new wear plates to railroad wheels,
Figure 2. After the wheel is mounted on its welding fixture, MV
provides location analysis to compensate for positioning variations and
differences from wheel to wheel that show up after old wear plate
removal.



Other opportunities



Another good application for machine vision is where you are
experiencing a high incidence of machine breakdown caused by oversized,
undersized, warped, misshaped, or misoriented parts. An MV system
upstream of a feeder mechanism can reduce, maybe eliminate, downtime by
rejecting unacceptable parts before they impact machine operation.



A situation definitely warranting machine vision is one that
requires maintaining a parts inventory because inspection may result in
rejecting a complete lot run based on statistical sampling. The 100
percent inspection level–practical with MV–assures you that virtually
every part passed by the system is good. This permits applying
“just-in-time’ inventory-control techniques with a
corresponding reduction in material handling time and damage that might
be experienced during handling. Likewise, MV can provide savings
wherever inspection is a bottleneck.



Similar to one of the basic justifications for robotics, machine
vision can be applied to operations in hazardous or unhealthy
environments. It generally has a better tolerance for loud noise,
elevated temperatures, heavy parts, and airborne contaminants, such as
metal dust or toxic vapors, than do humans. Conversely, vision systems
aren’t as prone to introduce contaminants into an operation,
whereas humans often are the source of dust, oil, and other debris
carried on hands and clothing.



Lastly, if you are faced with an operation that’s subject to a
number of errors caused by operator judgment, fatigue, inattentiveness,
or oversight brought about because of a dull job, MV can provide a
quality boost and cost savings. Whenever undertaking a major capital
expansion program, take a hard look at machine vision in lieu of alternative, less effective, often more costly methods.



But is it for you?



Most metalworking plants perform operations where MV can improve
quality and cut costs. The checklist of factors to consider when
selecting and implementing a machine-vision system (included in this
article) will help you decide where MV is a practical solution to your
vision-sensing problems. There also is a lost of factors to consider in
selecting and installing MV systems.



Being systematic when evaluating needs, developing specifications,
planning projects, and arranging for procurement and installation
virtually guarantees you will receive a properly designed system that
meets your machine-vision requirements.



Photo: 1. This Orbot system is designed to inspect bare
printed-circuit boards. It checks the inner layers, artwork, and resist
patterns to spot a reject condition at the point of 10 west value added.



Photo: 2. At a Norfolk and Western repair depot, this Automatix
Partracker system uses stereo techniques to precisely determine location
of a railroad wheel in a welding fixture. Data is fed back to correct
the path of the welding gun during placement of a new wear plate.

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