That's a excellent question. So we've seen this quite a lot because the
early adopters of Machine Vision, one of the number one complaint that
we see is, you know, we've been sold a system that says its X percent
accurate, it does all the things that it claim to do,we
installed it, everyone is excited, and, you know, but a few months later,
the promise doesn't live up to what is, the reality is very different than
what it started off with. You get some changes and then it starts to,
you get a lot of false rejections. So, false rejections are things which are
good acceptable parts, which are then rejected unnecessarily, and
then you just need to go and either re-inspect those parts or worst
still you have defective parts, which are then accepted. So how do we then
go back and then you're dependent on the vendor to come and correct this. So it becomes
a maintenance issue. Now, there's a couple of ways when we've seen
this quite a bit and that's the number one complaint that I've
seen from industries using machine vision. So a couple of things
I would , you know, suggest is to how we can minimize those disruptions. Now
one, there is a qualification processes and and methods that you use to ensure that Vision
system is working, has to be robust and strong. What do I mean by that is, when you teach
a system or you deploy a System, make sure that you have adequate representation
of all the different types of permutations, combinations
of, you know, possible defects or possible parts or possible
conditions that your system would have to inspect. So, you know, preparation
here is key. So let's go back to the cell phone example,
you're trying to inspect cell phones, now, before you, qualify or deploy a system and
let it run autonomously without human intervention, make
sure you qualify the system by using all the different types of parts
of cell phones with maybe end number of defect types that you have,
that you already collected and kept it as sort of this golden set
that you would then run through the system and make sure that the results
are as expected. So,defective part always classifies as a defect
and non-defective part always classifies as accepted.
Now, run through these not just in terms of variety, but also in terms of repeatability,
so run through them multiple times,so to also ensure there's repeatability. So those are
the things that you could do, you know, before you deployed
but having said that even to the best of your ability, you cannot practically
predict all kinds of permutations,combination,have them be ready,make
sure it is working you know, you're always going to encounter
some surprises after you've deployed. And we've seen things like lighting condition
changes or production process changes, which now change the finish of a particular
part. So what is earlier very smoothly finished
is now little roughly finished and things start to change or a new part gets introduced
or a new defect, gets seen, which earlier not seen before because now
you have all this data that you're capturing and, you know, you can, actually
trace back, those images or use those images to trace back your production
process. So, when these new things happen, you need
to be alerted of this sooner than later. You don't want a customer calling you and
say, hey, you ship me this bunch of defective things and you are like,
no, wait, but I had this vision system, its supposed
to catch all these defects and when you go back, you find that this was
a new defect which was never trained. You don't want the customer telling you that
this new defect was not caught, you want this caught earlier.
So what we've sort of done at Qualitas for instance is can we use an intelligent
way? So just like you use sampling based quality
control in your manufacturing line, can we QC the QC system, right, which is sampled,
these images that are being inspected but sample it intelligently. So
like, for instance, if there's a confidence score associated with an inspection,
right, or if we know beforehand that certain variants or SKUs are likely not
going to be very accurately judged. Can we sample more of those SKUs, using a
combination of these rules, you then collect those sampled inspected images
and present it to a subject matter or human grader and have them double-check
this to say that, this system said it's okay, but, you know,
the confidence was low. Is it really an okay part or did it have a
defect in it or vice versa? And now with this double-checking, you are,
then preventing any kind of mispredictions early on the process and you
may catch these early on, and then correct those retrain those and then
make sure that these surprises are then eliminated.So we call this a human
in the loop grading system that we have developed and which works very
powerfully to maintain these systems, correct errors, predict errors from happening
and you know, if you're thinking that you can just deploy
a Machine Vision system and then, you know, forget about it and then just,
you know ignore this completely then, you know, that's really ill-advised
as a process.So you need to have these systems in
place, but also intelligent systems, not just, you know, do random sampling or just go once
in a while and check is it working okay, but across the spectrum of these inspections
you want to sample these and then monitor these, using the least
amount of effort in order to predict and maintain and correct these systems.
So, that's my advice and also a practice that we follow very effectively here at Qualitas.