Circular Saw Kickback Killer (We used science to make tools safer) – Smarter Every Day 209

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In this lesson, Destin and Chad explore the dangers of kickback in circular saws and present a solution using a multi-axis detection system that employs machine learning to enhance tool safety. By utilizing sensors to detect kickback events and automatically engage a braking system, they aim to prevent injuries associated with this common issue. Their project, Lantern Safety Kinetics, advocates for the integration of this technology into all saws to improve user safety.

Circular Saw Kickback Killer: Making Tools Safer with Science

Welcome to an exciting exploration of how science can make tools safer. I’m Destin, and today I’m joined by my friend Chad. We’ve been working on a fascinating project for the past four months, and we’re thrilled to share it with you. Our goal is to eliminate kickback from circular saws, a common and dangerous issue for users.

Understanding Kickback

Kickback occurs when you’re cutting a large piece of wood, like plywood, and the cut pieces start to close in on the blade. This pinching can cause the saw to jerk back toward the user, posing a significant safety risk. If you’ve experienced it, you know how alarming it can be.

To demonstrate, we used a high-speed camera to capture the kickback in action. As the saw blade gets pinched, it lifts out of the wood and moves toward the user. In a real scenario, you’d want to release the trigger immediately, but human reaction times can be too slow to prevent injury.

Our Solution: A Multi-Axis Detection System

To address this, we’ve developed a multi-axis detection system that uses machine learning to detect kickback and dynamically brake the motor. Chad explains that the system uses a nine-axis accelerometer, gyroscope, and magnetometer to sense kickback events. By feeding data from numerous saw users into a neural network, the system learns to identify kickback patterns and respond accordingly.

In our demonstration, the saw stops itself when a kickback is detected, thanks to the braking system. This is achieved by analyzing the acceleration and magnetic field data, allowing the saw to apply the brake and prevent the blade from spinning further.

The Science Behind the System

Our system captures data from various sensors and uses a machine learning algorithm to make decisions. By annotating data from kickback events and regular use, the system learns to distinguish between the two and triggers the brake when necessary.

We tested the system by inducing kickback while the saw was running. The data showed a spike in acceleration and magnetic field when kickback occurred, prompting the brake to engage and stop the blade’s rotation. This innovative approach uses science to enhance tool safety effectively.

Future Implications

We believe this technology should be integrated into all saws to prevent kickback injuries. If you agree, share this information with your favorite tool manufacturers. Our project, Lantern Safety Kinetics, aims to make hardware smarter and safer.

Additional Learning: “Seveneves” by Neal Stephenson

On a related note, I recommend the book “Seveneves” by Neal Stephenson, available on Audible. It’s a captivating story about humanity’s survival after the moon explodes, exploring themes of technology and space exploration. This book has profoundly impacted my thinking, and I encourage you to check it out.

Thank you for joining us on this journey to make tools safer. If you enjoy this type of content, consider subscribing to Smarter Every Day. We have more exciting projects in the pipeline, and we hope you’ll continue to learn with us.

Stay safe and keep getting smarter every day!

  1. What are your thoughts on the potential impact of integrating a multi-axis detection system in all circular saws? How might this change the way people approach woodworking or construction?
  2. Reflect on a time when you experienced or witnessed a tool-related accident. How might the technology discussed in the article have altered that experience?
  3. How do you think machine learning and sensor technology can be applied to other tools or industries to enhance safety?
  4. What are some challenges you foresee in implementing this kickback prevention technology on a large scale? How might these challenges be addressed?
  5. Consider the role of human reaction time in preventing accidents. How does the technology described in the article compensate for human limitations, and what are its implications for safety?
  6. Discuss the importance of data collection and annotation in developing machine learning systems. How does this process contribute to the effectiveness of the kickback detection system?
  7. How do you think the themes explored in “Seveneves” by Neal Stephenson relate to the technological advancements discussed in the article?
  8. What are your thoughts on the future of tool safety and the role of innovation in preventing accidents? How can individuals contribute to this progress?
  1. Activity 1: Analyze Kickback Scenarios

    Review high-speed footage of circular saw kickback events. Identify the key moments leading up to the kickback and discuss how the multi-axis detection system could intervene. This will help you understand the mechanics of kickback and the importance of timely intervention.

  2. Activity 2: Sensor Data Interpretation

    Examine sample data from the nine-axis accelerometer, gyroscope, and magnetometer. Work in groups to interpret the data and identify patterns that indicate a kickback event. This will enhance your data analysis skills and deepen your understanding of sensor technology.

  3. Activity 3: Machine Learning Workshop

    Participate in a hands-on workshop where you will train a simple machine learning model to recognize kickback patterns. Use annotated data to teach the model and test its accuracy. This activity will provide practical experience with machine learning applications in safety technology.

  4. Activity 4: Design a Safety Feature

    In teams, brainstorm and design an additional safety feature for circular saws. Consider integrating other technologies or improving existing ones. Present your design to the class, explaining how it enhances user safety. This will encourage creative thinking and innovation.

  5. Activity 5: Discuss Future Implications

    Engage in a group discussion about the future implications of integrating machine learning into power tools. Consider the potential benefits and challenges, and how this technology could be applied to other tools. This will help you think critically about the broader impact of technological advancements.

Sure! Here’s a sanitized version of the transcript, with inappropriate language and any sensitive content removed:

Hey, it’s me, Destin. Welcome back to Smarter Every Day. This is my buddy Chad.

– Hey.

We are absolutely excited 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, so there’s a patent pending product that we’re working on. What it does is it eliminates kickback from handsaws. Chad, do you want to explain what’s going on here?

– Yeah, so you’ll be cutting 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. When the blade gets pinched, it can become dangerous and come back towards you.

It’s a big deal. If you’ve ever had it happen, you know exactly what I’m talking about. We’re going to 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 because 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 chain’s not going to 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 that rotation 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.

– [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 you. 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 leapt 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 oftentimes humans simply can’t react that quickly.

Okay, so that’s kickback. It’s very scary. Now what we’re going to 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 brake the motor.

– Yeah, so I can give you a demo on the first one that I built. So I’m going to 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 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. 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.

– [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 amazing 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. At that point, that tells the algorithm, 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.

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 going to 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.

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.com/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 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.

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 want to 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.com/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 going to make your life better, and that’s why I love my collaboration or partnership, whatever you want to call it, with Audible; they’re great.

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 going to be amazing.

If you want to 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 them, and I hope you like them too. I’m Destin, you’re getting Smarter Every Day. Have a good one, bye.

(gentle music)

Let me know if you need any further modifications!

KickbackA sudden forceful recoil or backward movement, often experienced in mechanical systems when an unexpected resistance is encountered. – Engineers must design mechanisms to minimize kickback in power tools to ensure operator safety.

SawA tool or device with a serrated blade used for cutting hard materials, often powered by an electric motor in industrial applications. – The precision saw in the engineering lab is used to cut metal sheets with high accuracy.

SystemA set of interacting or interdependent components forming an integrated whole, often designed to perform a specific function. – The cooling system in the reactor is crucial for maintaining optimal operating temperatures.

DetectionThe process of identifying the presence of a particular substance, object, or condition, often using specialized equipment or techniques. – Advanced detection methods are employed in particle physics to observe rare subatomic events.

MachineA device consisting of fixed and moving parts that modifies mechanical energy and transmits it in a more useful form. – The CNC machine in the workshop is programmed to produce complex components with high precision.

LearningThe acquisition of knowledge or skills through study, experience, or teaching, often applied in the context of adaptive systems. – Machine learning algorithms are revolutionizing data analysis in engineering fields.

SafetyThe condition of being protected from or unlikely to cause danger, risk, or injury, especially in engineering environments. – Implementing rigorous safety protocols is essential in chemical engineering to prevent accidents.

SensorsDevices that detect and respond to physical stimuli such as temperature, pressure, or motion, and provide corresponding output signals. – Sensors in the automated assembly line ensure that each component is correctly positioned before welding.

AccelerationThe rate of change of velocity of an object with respect to time, a fundamental concept in dynamics. – Engineers must consider the acceleration of vehicles to design effective braking systems.

TechnologyThe application of scientific knowledge for practical purposes, especially in industry and engineering. – The latest advancements in battery technology have significantly improved the range of electric vehicles.

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