ColorSchool.org
Dedicated to the art and science of color
QUALITY
Dedicated to the art and science of color
QUALITY
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CMC Color Tolerancing
For manufacturers, the desire and practical need to establish a color
tolerancing system is imminent. While the optimum system for tolerancing
would be a visual method, the subjectivity of establishing a consistent visual
system is nearly impossible.
Ideally, we would hope to achieve an instrumented system of evaluation that
represents (and agrees with) what we see visually. This is the theory behind
CMC color tolerancing. CMC allows the quality professional to establish a
single numeric target that results in a pass/fail outcome.
The key to CMC is that is relies on elliptical tolerancing (LCH math) and
adjusts the size and shape of the ellipse according to where the color falls in
the color space.
For example, a colorant that is high in chromaticity will, generally, reveal a
larger tolerancing ellipse than a colorant with lower chromaticity. This is
because the human eye is less capable of identifying color variation in high
chroma samples than it is in low chroma samples.
If we place this theory in a graphical model, it looks like what we see in the
figure below.
For manufacturers, the desire and practical need to establish a color
tolerancing system is imminent. While the optimum system for tolerancing
would be a visual method, the subjectivity of establishing a consistent visual
system is nearly impossible.
Ideally, we would hope to achieve an instrumented system of evaluation that
represents (and agrees with) what we see visually. This is the theory behind
CMC color tolerancing. CMC allows the quality professional to establish a
single numeric target that results in a pass/fail outcome.
The key to CMC is that is relies on elliptical tolerancing (LCH math) and
adjusts the size and shape of the ellipse according to where the color falls in
the color space.
For example, a colorant that is high in chromaticity will, generally, reveal a
larger tolerancing ellipse than a colorant with lower chromaticity. This is
because the human eye is less capable of identifying color variation in high
chroma samples than it is in low chroma samples.
If we place this theory in a graphical model, it looks like what we see in the
figure below.
While the theory of CMC can be fairly easy to understand, implementing it can
be complicated. This is usually because of two reasons. The user has
already established a color management system (usually CIELab) and has
many years of data, of which color decisions are made. The user also has
established target or standard samples (often derived from customer
feedback). These samples are sometimes not representative of the center of
the population of acceptable color. The latter condition must be resolved in
order for CMC to be effective.
Here are a couple of links that can better explain CMC and a simple
approach to implementing CMC as a color tolerancing system.
Additional training for CMC color tolerancing and its implementation isavailable through ColorSchool.org. Visit our Training page for more
information.
be complicated. This is usually because of two reasons. The user has
already established a color management system (usually CIELab) and has
many years of data, of which color decisions are made. The user also has
established target or standard samples (often derived from customer
feedback). These samples are sometimes not representative of the center of
the population of acceptable color. The latter condition must be resolved in
order for CMC to be effective.
Here are a couple of links that can better explain CMC and a simple
approach to implementing CMC as a color tolerancing system.
 | What is CMC? | |
| How to Implement CMC? |
Additional training for CMC color tolerancing and its implementation is
information.