Compliant Mechanisms that LEARN! - Mechanical Neural Network Architected Materials

This video introduces the world’s first mechanical neural network that can learn its behavior. It consists of a lattice of compliant mechanisms that constitute an artificial intelligent (AI) architected material that gets better and better at acquiring desired behaviors and properties with increased exposure to unanticipated ambient loading conditions. It is a physical version of an artificial neural network used in current machine learning technologies.

To learn more about the content of this video, I encourage you to read the following publications, which can be accessed at the provided links:

[1] Lee, R.H., Mulder, E.A.B., Hopkins, J.B., 2022, “Mechanical Neural Networks: Architected Materials that Learn Behaviors,” Science Robotics, 7(71): pp. 1-9

[2] Lee, R.H., Sainaghi, P., Hopkins, J.B., 2023, “Comparing Mechanical Neural-network Learning Algorithms,” Journal of Mechanical Design, 145(7): 071704 (7 pages)

Part files to fabricate the mechanical neural network can be downloaded on Thingiverse using this link:

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Acknowledgements:
Special thanks to Ryan Lee, Erwin Mulder, and Pietro Sainaghi who helped fabricate, test, and simulate the mechanical neural network in the video. I am also grateful to my AFOSR program officer, “Les” Lee, who funded the research that this video features.

Brain Scan Attribution:
Microstructure Image Attribution:
Body Armor Attribution:

Disclaimer:
Responsibility for the content of this video is my own. The University of California, Los Angeles is not involved with this channel nor does it endorse its content.

[mrmurphymil]

at 11 minutes I realised this was a research paper in an easily digestable and widely available format, great work.

[BenFitz]

As a mechanical engineer who is learning computer science and machine learning, this is an amazing bridge between the two worlds! I cant wait to print some and play with the concept myself. The applications are truly endless, I wonder how long until this is made microscopically, and applied everywhere.

[blacklistnr]

This is an incredible combination of an entertaining youtube video and a technical paper presentation! I wish more articles were presented like this

[x.khann.x]

My heart goes out to the graduate students who did all this work. You guys are ferocious, you deserve only the best in life.

[etunimenisukunimeni]

I went from complete "what is this I don't even" to "okay this makes sense, cool" in 20 minutes. Very well presented, super interesting and understandable even to someone with zero experience in mechanical engineering.

[mrmurphymil]

This format needs to be the standard for research papers going forward

[jamespray]

This is amazing. Miniaturized / nanoscale applications of this really could drive world-changing metamaterial developments. It's also a very helpful way to unpack and visualize the fairly opaque world of learning neural networks in general. I never mind waiting for content like this. Thanks so much for the walkthrough!

[michalchik]

In a general sense this is what bone and connective tissues do. They have built-in stress sensors that look for electrical signals that appear in weak spots in the bone and connective tissue. They rebuild the structure to fix those weak spot s and redistribute load.

[poipoi]

This is insane. Soon we'll be doing this kind of stuff with photolithography. Perhaps it'll be the next step in neural networks as a whole to increase efficiency.

[caiobortoletto]

There are people that legitimately think that going to space is so crazy that we havent done it. Meanwhile, were doing this. Its nuts

[the.original.throwback]

The joys of turbulence and material science continue. It is interesting to contemplate where and how nature employs similar functions in organism behaviors.

[dorotabudzyn]

This is fantastic way to present your paper. Very interesting research, I am looking forward to more work from your lab!

[smoothmidnightfudge]

Materials Science and Engineering dropout here. I couldn’t hack it in academia at that level, I had the smarts but it was too much stress and pressure. But I still love the subject matter, I think it’s absolutely fascinating, and stuff like this video is what sent me into that field in the first place. Thank you for the detailed breakdown, this was awesome to watch.

[Sazoji]

I wonder if you could use plant cells to do something like this. Have a gas-filled vacuole inflate/deflate across a uniform foam of cells, which alters the tension against the cell walls, allowing for control over the material stiffness.
plants already do this naturally to grow twards light, but imagine it being used as an organic wing. I imagine it would be made up of something like cactus flesh, filled with a microfluidic network to control local stiffness.

[Blayzeing]

Absolutely fantastic! I look forward to seeing this get progressively miniaturised.

[achpek]

This idea worth a Nobel prize! Great job, guys! In the future we will create a metamaterial that can morph into anything and be controlled by brain. This is real deal, I must say as an engineer.

[cubicengineering]

Very interesting! Though it feels like there will be a lot of problems with miniaturising this type of system. My intuition tells me that most miniature things wouldn't be tunable by the connections between nodes, but rather the nodes themselves. For example I could imagine a theoretical case where each node has some sort of "pressure" that it applies universilly to all of its neighbors. It may even be as simple as laying out a latice of beads either of different materials, or hollow with different air pressures or wall thicknesses.

Thus, what I would be most interested in seeing next is simulating a node-pressure centric model, to see if changing the adjustable factors from the beams to them would still be able to produce the behaviours that were exhibited in this video.

[dinhero]

This is an idea that I had I wanted to share with yall. This idea has been partially implemented in the video but I want to extend it. What if instead of optimizing the model in the real world you created a computer simulation that would give you more accurate results and a much faster interface (because it's software <-> software instead of software <-> real world). Now that you are doing the simulation part purely digitally you don't really need such a complicated mechanism to vary the stiffness. Instead, you could export the result of the computer simulation in a format readable by 3D printers. Instead of your current mechanism, you could have something like a coil that could be stiffness-manipulated by varying its width. Now, yes, this is a much less "dynamic" approach because it does not allow you to change the values on-the-fly and requires you to 3D print your material every time you want to test it in the real world but as long as your Simulation -> Real World process is accurate enough you should not need to 3D print your material every time you want to test it and should be able to do it using only software and only need to 3D print it when you want to be absolutely sure that the material behaves as it should.

[cougarten]

I guess after trying the dynamic learning you could (mass) produce a hard-coded version with the same values and just 3D printing :)

[xzendon]

You should be able to manufacture a much cheaper and easier to scale version of this by using electro-osmotic cells (cellulose membrane tube with internal electrode between two plates is probably the simplest) as the stiffness altering actuator. Simply increase the voltage on the cell to increase the internal pressure.