It's a very specialized application, the camera has actually a little role to
play in success of a machine vision system. Now, today's camera technology has become
so advanced and its commoditized that most camera makers or most camera products
that you would see in the market, do a pretty good job.
Now if it's good enough for a human to look at the image and identify
features or identify the result that you want to inspect
then, its likely good enough for the software. the camera really has not so much of an impact
when it comes to success, there are other more important
criteria like your lighting as well as your software which is even more
impactful or critical in showing the success of the system.
So, inaccuracies or errors in Mission Vision is really categorized
into two different buckets, False positive, and false negatives.
So if you have a system that is actually identifying defects effects so
in another words, the system has to flag the detection of a defect, so its identifying
defects. So when its a positive, it actually means
it has identified that part as a defect when its done falsely, so in other words false
rejection that's known as false positive. on the other hand, when you say negative,
so its categorized that product as not a defect or an acceptable part, now
its done falsely, that means its falsely accepting a defective part
As you can imagine, a false negative is a more dangerous kind of defect for you.
So you always want to error on the side of caution to ensure that there are no false
negatives on you inspection lines. However, sometimes due to an oversensitive
Machine Vision, you may actually end up seeing a lot of false rejections
or false positive and the way to counter that is really by analyzing the kind of false positives.
You have or you have seen. Now if its an AI based or Deep learning based system
A way to check this would be to ensure that the features to analyze the features or to
see whether it belong to certain category.
So it may be due to not enough training data or balanced training set that you have not
trained the system Adequately enough. But it's important for
you to actually categorize those images and have a very any disciplined process
to analyze them, to inspect, all those images manually to be able to then
find an intervention to counter these false positives. Often times,
the mistakes that I see people make is. that they look at a very small subset of images
and then try to engineer that based on the smallest subset and this may
solve the issue for a small subset may be because of the variant
or because of the certain type of defect But then you tend to ignore the other categories
which you've not really engineered this for. So, when you look at this analysis make sure
that you see across broad variants or across all your permutation and combination and then
ensure that mitigation in terms of training are
implemented so that we can arrest those false positives in an accurate way.
So, the main objective is to ensure that you're replacing your manpower, Isn't it?
But, practically speaking, that's not going to happen overnight.
So when your implementing a machine vision system, you have to ensure that there is some
adequate supervision of the system itself. Now, you're probably better off eliminating,
Human Resources, in a phase manner. So if you have multiple lines
and you have multiple teams inspecting this, you may want to replace or cut down on this
in a phase manner, then you have one person supervising all three lines.
So in other words you could have some sample inspection that's
happening after the inspection, by the machine vision system or
you could be inspecting all the rejections to ensure that there are no false rejections.
So in that process, staggering reduction of the Manpower is lightly
be a better bet until you see full confidence in the system.
But, even then you always want to make sure you are doing some level of monitoring
to ensure that your vision system is always performing with expected accuracies.
you could never want to let this go unmonitored because you never know when
you're going to run into surprises. So the skill required to develop a Machine
Vision system and to implement a machine vision system is actually
very different. You don't need to be an expert in implementing
a system but you need to have sufficient expertise that when you outsource
this, or your managing the project for machine vision system
You have the expertise to evaluate and make sure that it ends up in a successful install.
So what skills are really required to monitor and drive a project communication?
Well, the first one is, you need to understand the basics of Machine vision
because there are so many systems out there Smart camera to PC based, AI based systems
to rule based systems And often times, I see that there's a lot
of confusion around, what the difference is between all of that, so
you end up comparing apples and oranges, a smart camera system with an AI based system
and you end up in a wrong choice and you end up not seeing success.
So firstly, make sure you understand what is being proposed by the different vendors
or by the different technology providers, for
which, you are evaluating these system vendors. So, once you have figured that out, the next
thing is to ensure that, you know, what is going to be
the measure of success. Any system that comes in, has to be monitored for a
given sufficient amount of time to ensure that all your inspection criteria,
are sufficiently and accurately met. So it may be due to practical considerations,
not all of your SKUs are being planned for production, so make sure
you have a project chart that communicates with the vendor or communicates
with the project team that you would like to see at least a
certain percentage or certain number of these different SKUs, go through
your inspection system, and correctly classify these for defects and
that may happen over a larger period of time Now, if your SKUs or if your defect inspection
parameters are very similar then you may choose to actually do this for a subset of SKUs or
variants and assume that, with that the qualifications will be
sufficient to Predictably handle the future SKUs that are going to be drawn into your
line. So, the third skill is really ensuring that
you are able to manage your vision system in terms of
changes to your production plans. So it could be addition of variants, it could
be removal of variants, it could be changing the sensitivity of the defect or
it could just be generating a report and then being able to
analyze it for root cause, so you need to have enough skills to be able to monitor
as well as making changes to the system so that we are not dependent
on the vendor or the third party. that way you will be in full control and ensure
that you are maximizing the use of vision system successfully.