Machine vision has been instrumental to automating quality assurance thanks to its ability to locate, identify, and inspect products through computational image analysis.
Conventional computational image analysis has its limits, however, which has traditionally limited application of
machine vision to component inspections. When the component is part of a larger assembly, a complex package, or
a kit—such as an automotive assembly, circuit board, or
surgical kit—then random product placement, variations
in lighting, and other factors can overwhelm the computations of conventional machine vision systems. Consequently, final inspection of assemblies, packages, and kits
has largely remained a manual operation.
While human inspectors excel at validating complex as-
semblies, their abilities are subject to fatigue. Studies show
most operators can only focus on a single task for 15 to 20
minutes at a time. Qualified inspectors are also becoming
increasingly harder to find. In 2018 more than 63% of man-
ufacturers said they were having difficulty staffing their as-
sembly lines, according to Assembly Magazine’s 2018 State
of the Profession report. That’s 16 percentage points higher
than the previous year’s figure. It’s also worth noting that
inspectors tasked with quality assurance often rise from the
ranks of the best assemblers.
While labor shortfalls explain the motivation to find automated inspection solutions, manufacturers hoping to leverage machine vision still face several challenges when applying
it to inspect assemblies, packages, and kits.
THE LIMITS OF CONVENTIONAL
Vision solutions are limited in their ability to handle part-
to-part variation, for example. Components tend to vary one
from the other in small ways. This is not an issue if the vari-
ance is a bent or missing lead on a semiconductor chip. But
it is much more difficult to program a machine vision system
to detect variances among several components in a finished
assembly, or even identify cosmetic differences on a single
component, such as a blemish on a casted or machined part.
Conventional machine vision can also struggle to validate
the correct relationship between multiple assembled parts.
An automotive motor transmission, for instance, incorporates hundreds of individual parts and dozens of critical components. Programming a vision system to confirm the size,
location, and position for every critical component is time
consuming and impractical.
If these challenges were not difficult enough for conventional machine vision systems, consider also that assembly,
packaging, and kitting lines undergo regular changeovers
in response to new sales and production requirements.
With each changeover, new components may be introduced to a kit, or similar parts might be packaged a different way to accommodate a specific customer’s needs.
By John Petry
Spreads Automated Inspection to Assemblies,
Packaging, and Kitting