- Hey, it's me, Destin. Welcome back to Smarter Every Day. This is my buddy Chad. - Hey. - We are absolutely giddy
because we've been working on something for how long? - 12 years. - Well, I'll be like that's us hanging out but we're working on this
project for how long? - I don't know, a good four months. - Four months. Chad's really good at
software and we had an idea and so there's a patent pending product that we're working on. What it does is it eliminates
kickback from handsaws. Chad, do you wanna explain
what's going on here. - Yeah, so you'll be
cutting a piece of big a big sheet of plywood and
as you get down towards the middle of it the pieces
that you've already cut start to bend towards each
other and they'll pinch the blade and when the blade
gets pinched it becomes kind of a wheel and these
teeth, since they're coming back towards you, catch in
the wood and then the whole saw comes back at you. - It's a big deal. If you've ever had it
happen, maybe a couple drops of pee came out. - Terrifying. - You know exactly what I'm talking about. We're gonna show how it works
first with the high speed camera and then we'll
show you the fix we have for kickback. - [Chad] Turning it on, three, two, one. (motor rumbling) It wants to go. - [Destin] It wants to go? I'm ready. - Three, two, one. - [Destin] Whoa, it's still on, kill it. What are we doing? - [Chad] And it came out
'cause it got to the knot. - You notice we have
this chain and we've got this danger radius there. Will you show and make
sure that the chains not gonna hit the, yeah. (motor revving) (gentle music) - Oh, yeah. - You need something for
these safety glasses. Because of the rotation
direction of the blade, the saw is lifted up out
of the workpiece and then that rotation, again,
causes it to be propelled towards the user. Now obviously, if this
were to really happen you would want to release
the trigger on the saw and in this test we have it
zip tied so we can actually see what's happening
but I found, personally, that my body tenses up when I get startled and I'm not so sure that I
would have enough control in the moment to make the
decision to release the trigger. Let's look at another run
and I'll stand just outside the danger zone and I'll
pull focus on the camera lens and we'll see if we can measure
the human response times required to avoid injury. Whoa. - That's scary. - [Destin] Okay, in this run,
look at the timer in the top right of the screen that's
counting from the moment the saw starts rising up out of the wood. You can see that the
blade is fully released from the wood within 80 milliseconds and it's already headed up
towards your gonadular region. Assuming that you don't
tense up, you would need to respond by releasing
the trigger well within a tenth of a second. Once the blade is freed from
the wood, there's no longer any resistance and therefore
the blade increases speed because the motor is adding angular momentum to the system. Now that makes it even more
dangerous and you can see here when the blade falls
back down and comes back in contact with the wood,
just imagine if that was flesh and keep in mind here that
this is a slow-mo video. All of this that you're
seeing here is happening in just a tenth of a
second after the blade has lept up out of the wood. Obviously the ideal time
to respond to a kickback event is before the saw
even leaves the wood. The problem with all this
is often times humans simply can't react that quickly. That is no bueno. It's a bad day, man. Okay, so that's kickback. It's very scary. Now what we're gonna do is
show you what we've made. What would you call it? It's a multi-axis detection
system that can figure out kickback based on machine learning. - Keep going. - [Destin] And basically
dynamically break the motor. - Yeah, so I can give you a demo on the first one that I built. So I'm gonna turn it on
and let go with my finger and you'll hear it turn off quickly. See that? - [Destin] Yeah. - If there was no brake,
it would just keep it would keep spinning. So I put a sensor in
there to sense a kickback so that when I do that it shuts off automatically. - [Destin] So your finger
was still on the trigger but you just accelerated it backwards. - Right, this was the
first one that I built and since I just used an accelerometer there are some false alarms like if you bounce it
around or just normal use it can trigger and the new one that I did I used a nine-axis accelerometer, gyroscope and magnetometer. So the really fancy thing
that we're doing here is we're doing a machine
learning algorithm. - [Destin] So you don't care. - I'm not really programming in exactly what the thresholds are
that I need it to sense. I'm taking data from dozens
to hundreds of people using a saw in normal, everyday ways and feeding that information
into a neural network and I'm comparing, stop grinning. - We're cutting plywood here. - So machine learning works
by you take a big data set of people using it in
normal ways and then another big data set of kickback
events and the computer automatically takes all
those, all those data sets and figures out what the anomalies are. What's unique to kickback that's
different from normal use. - [Destin] So you hogged out
the handle it looks like. - [Chad] I did. - [Destin] And so there is
a legit computer in there. It has accelerometers and gyros in it. - [Chad] Yep. - Three, two, one, go. So it stopped itself? - Yeah. - It stopped itself. That's what it does? - That's what it does. - That's awesome. - [Chad] To better understand
how the system works, we've overlaid the data
on top of the slow motion. We're measuring nine
different sensors here but for simplicity's sake let's just look at the overall magnitude
of the acceleration which is in yellow and the magnetic field which is in red. You can see that when the saw itself, not the blade, but the saw
itself starts to accelerate the saw's braking system
is applied and the magnetic field starts to work against
the rotation of the blade. The cool thing about using magnetometers is that the algorithm
might be able to detect the magnetic field from
the motor and then make decisions based on that
information as well. - [Destin] And so you just
combined those different things together and
make it make a decision. - Right, I just let it
capture all that data. I don't tell it what's important. After the fact, if it's
been a kickback, I annotate the data with this was a kickback event and then I have a whole
bunch of other files that are regular use
cases and it figures out what's important in the kickback event to trigger output. - [Destin] That's awesome dude. - Yeah. - [Destin] Three, two, one. - Ooohh. - It did stop, though. - [Chad] So we performed
all of those tests by starting the saw while it was already bound up in the wood but what would happen if we were to spin the saw up first before we induced the kickback event? This graph is stinkin' awesome
and it tells the whole story. Let me walk you through it. Okay, if you look back here on the left you can see this red spike. That's when the motor was first spun up. That's the torque
associated with accelerating that blade rotationally,
right, so as we move along here and we bump the front
of the saw, that induces a kickback event and you
can see that in the data by this large yellow
acceleration and at that point that tells the algorithm
oh, oh, there's kickback and so it applies the
brake and you can see this red spike in the magnetic field. That is the saw's brake
being applied and trying to stop that rotation of the saw blade so you can actually see
that happening and it tapers off as the velocity of
the saw blade decelerates. This is awesome. I mean like high five. We just used science to
stop a kickback event. Well, I mean, it's even
cooler because it's high five and not high four
which is what could happen if the kickback, I'll shut up now. - Okay, here's the final test. I know how to run a
circular saw the correct way and I also know how to do it the wrong way so I'm gonna try to induce
kickback, with the algorithm running, from my hand
and see what happens. Okay here we go. It stopped. This should be on saws. If you can detect the
profile of a kickback event in Newtonian physics
then you can implement the brake that's already on the saw. - Yeah, so the brake's already in there. All you need is a little
sensor to figure out when to hit the brake and
that's all we're doing. - Yeah so if you think
this needs to be in tools please tweet this video
to your favorite tool manufacturer and then tool manufacturer, come talk to us. We'll put a link down in
the video description. - Wait till you see the
chainsaw experiment. (beep) - I'm about to tell you something that's a pretty big deal in my life. It's a book called Seven
Eves by Neal Stephenson. I listen to it on Audible
which sponsored this episode. You can get Seven Eves by Neal Stephenson by going to audible dot com slash smarter or texting the word smarter to 500 500. The moon blew up without warning,
and for no apparent reason which is like the best
opening line to any book for my brain ever. The whole premise of the
book is the moon is exploded and humanity has to get off the earth. The technology developed
in order to do that, we could totally do this. There's a swarm of spacecraft in orbit and if you change your orbit
on one side of the earth just a little bit that
affects it on the other side as well, right? But if you have a swarm you
have to account for all of that. They developed something
called the perambulator and I read this book
like eight months ago. I still think about the
perambulator because the orbital mechanics hold true. Whip dynamics, there's
artificial intelligence used in robots to help mine asteroids. It's amazing so I know
there's supposed to be a movie adaptation of this book so Ron
Howard, if you're listening, I wanna be in the movie. Whatever, I'm not faking this. I really like the book. I love the fact if I'm
listening while I'm driving which is how I listen to audiobooks, I can swipe my finger and tap a button and that's how I can
save an audio bookmark. I can listen to it later. It's an amazing book. Seven Eves by Neal Stephenson. Get this by going to audible
dot com slash smarter or text the word smarter to 500 500. That book will change
your life and it'll change the way you think. The thing I like about this
is it's gonna make your life better and that's why
I love my collaboration or partnership, whatever
you wanna call it, with Audible, they're rad. If you're the kind of
person that subscribes to YouTube channels then
I hope you would please consider doing that with Smarter Every Day if you feel like this
kind of content earns it. If I've already earned your subscription then I would like to try
to work on convincing you to click the bell
because the next few videos are redonculous, like, we're
starting with rocket saw and it just goes up from there. It's gonna be amazing. If you wanna learn more
about the stuff Chad and I are doing together, we call
it Lantern Safety Kinetics. There's a link down in
the video description. The idea is to put brains in your hardware and make stuff safer. Thanks for watching these videos. I really like making 'em
and I hope you like 'em too. I'm Destin, you're
getting Smarter Every Day. Have a good one, bye. (gentle music)
I have zero experience with saws, but after seeing this video not long ago I became aware of the destructive potential of the saw blade getting pinched, and how fast it occurres. Well beyond our ability to react.
However is it really necessary to resort to machine learning to predict such occurrences? Is it really that complicated to predict? How much does one kickback differs from another. I'd imagine they'd follow the same path. Wouldn't just logging the data from multiple incidents watching for a common features and identifying based on that?
Sure, machine learning would also work. However isn't it an overkill? This safety feature has to be reliable, and my gut tells me whenever reliability is crucial, to stick to a more simple design.
And another thing. May I suggest you'd add a self-diagnostic test so that the user would be able to validate that the system is operating. If you're going to rely on microcontrollers to protect yourself from losing a couple of fingers, you may as well add a "test" button that runs a diagnostic procedure. Wiggling the saw to see if it locks doesn't seem very safe.
As someone who typically argues against a lot of the safety hand-wringing when active safety systems (like sawstop) come up, this makes complete sense to me. Using a circular saw is so much more liable to result in kickback -
I would buy a saw that had this system. I'm glad you mentioned chainsaws, and hope you're actually working on that application, because that's what immediately occurred to me after seeing it in action.
Several DeWalt tools implement this type of protection already. They have a very similar feature implemented on their FlexVolt grinder as well as several drills that detect rapid rotation of the handle and reduce torque to prevent the tool from breaking the users arm if the bit binds up. It's called "Perform and Protect." Here's a hyper masculine ad about it.
Is there a place where we can read through the code for this or is it closed source?
Will this work on chainsaws, and maybe, certain lawnmowers?
This is amazing! So glad you guys are working on this.
What inspired you guys to tackle this problem?
I'm always hesitant to trust an electronic (especially computerised) solution over a mechanical one.
Now I didn't think this idea all the way trough, but can't there just be a spring-loaded metal/tough cover for the blade that you need to load to use the saw that releases when button is pressed and there's nothing holding it back, but not when you're sawing?
Something like this?
Can have a safety on/off switch for when you start sawing.
And it would require an extra step, but it'd be more reliable, and you would be able to test it before starting work.
Very cool. How did you collect the data to train your model? Did you make it yourself and if so, how many times did you have to jam the saw for it to be accurate enough?