Printing Functional Systems Worlds Within Worlds Hod Lipson Mechanical & Aerospace Engineering Computing & Information Science Cornell University Computational Synthesis Lab http://ccsl.mae.cornell.edu Cornell University College of Engineering

Adaptation Changing environments, tasks, internal structures Behavioral adaptation Morphological adaptation

Breeding machines in simulation Lipson & Pollack, Nature 406, 2000

Emergent Self-Model Bongrad, Zykov, Lipson (2006) Science, in press

Damage Recovery With Josh Bongard and Victor Zykov

Making Morphological Changes in Reality

Printable Machines

Multi-material processes Continuous paths Volume Fill High-resolution patterning, mixing Thin films (60nm)

Illustration: Bryan Christie Multi-material RP Illustration: Bryan Christie

Our RP Platform Fabrication platform: (a) Gantry robot for deposition, and articulated robot for tool changing, (b) continues wire-feed tool (ABS, alloys), (c) Cartridge/syringe tool

Printed Active Materials Some of our printed electromechanical / biological components: (a) elastic joint (b) zinc-air battery (c) metal-alloy wires, (d) IPMC actuator, (e) polymer field-effect transistor, (f) thermoplastic and elastomer parts, (g) cartilage cell-seeded implant in shape of sheep meniscus from CT scan. With Evan Malone

Zinc-Air Batteries With Megan Berry

Zinc-Air Batteries

IPMC Actuators

IPMC: Ionomer Ionomeric Polymer-Metal Composite “Ionic polymer” Branched PTFE polymer Anion-terminated branches. Small cation

First printed dry actuator Quantitative characterization Improve service life Reduce solvent loss Reduce internal shorting Improve force output, actuation speed

Embedded Strain Gages Silver-doped silicon Robot finger sensor

IPMC: Ionomer Ionomeric Polymer-Metal Composite “Ionic polymer” Branched PTFE polymer Anion-terminated branches. Small cation

First printed dry actuator Quantitative characterization Improve service life Reduce solvent loss Reduce internal shorting Improve force output, actuation speed

IPMC Actuators

Results Power [W] Force [mN]

100% Printable Robot

With Daniel Cohen, Larry Bonassar Multi-material 3D Printer CAT Scan Direct 3D Print after 20 min. Sterile Cartridge Printed Agarose Meniscus Cell Impregnated Alginate Hydrogel Multicell print

The potential of RP Physical model in hours Small batch manufacturing New design space Design, make, deliver and consume products Freedom to create

Learning from the history Similarity with the computer industry In the ’50s-’60s computers… Cost hundreds of thousands of $ Had the size of a refrigerator Took hours to complete a single job Required trained personal to operate Were fragile and difficult to maintain Vicious circle Niche applications  Small demand Small demand  High cost  Niche applications Digital PDP-11, 1969 Stratasys Vantage, 2005

Exponential Growth Source: Wohlers Associates, 2004 report RP Machine Sales Source: Wohlers Associates, 2004 report

The Killer App? Honeywell’s “kitchen Computer”

Robust Low cost Hackable

Fab@Home Precision: 25µm Payload: 2Kg Acceleration: 2g Volume: 12”x12”x10”

Fab@Home

Fab@Home: “Fablab in a box”

www.FabAtHome.com

Digital Structures

Reconfigurable systems Fukuda et al: CEBOT, 1988 Yim et al: PolyBot, 2000 Chiang and Chirikjian, 1993 Rus et al, 1998, 2001 Murata et al: Fracta, 1994 Murata et al, 2000 Jørgensen et al: ATRON, 2004 Støy et al: CONRO, 1999 Zykov, Mytilianos, Adams, Lipson Nature (2005)

Programmable Self Assembly Stochastic Systems: scale in size, limited complexity Whitesides et al, 1998 Winfree et al, 1998 [Josh]: ok guys, before we jump into the hardships of hardware, lets see what this system might look like in simulation. [tell the story of reconfiguration as movie plays]. As you can see, we’re not just interested in self-seembly – we’re interested in dynamic, programmable, reconfiguration. There are many interesting algorithmic challenges, and we explore some of these in the paper: Key results are… So Paul, did we get any of this to work in hardware? …

Saul Griffith, Nature 2005

Hardware implementation: 2D [Paul:] ***In 2004, we showed two stochastic self reconfigurable systems in 2D: Each module had three degrees of freedom, and the structure assembled into a 2D pattern and then reconfigured using only “Brownian motion”. We used one system with swiveling permanent magnets, and one ‘solid state’ system with electromagnets. Kelvins in University of Washington recently achieved formation of patterns using 6 unit, and is also exploring how graph grammars can be physically implemented in such systems. Solid state implementation (no moving parts) are important for scalability into microscale. So Paul, what would be a natural next step to take? [Paul:] ***Well Viktor, here we will explore reconfigurable self assembly of systems with 6 Degrees of Freedom. And more importantly, we seek a method of self assembly and reconfiguration that scales well to the microscale. I believe Daniela Rus is also working in this direction. White, Kopanski & Lipson, ICRA 2004

Implementation 1: Magnetic Bonding [Paul]: Two different sets of experiments were run to demonstrate self-reconfiguration and to gather statistics about the mean time to bond. Start the movie In the first experiment, one module is placed on the substrate while the other is placed at an initial condition that allows for repeatability in experimentation. The experiment begins by supplying power and agitation to the system. When the module on the substrate powers on, it detects that it is the first module in the structure and waits for a dormant module to attach to it. When another modules attaches to first module, both modules detect one another and the module on the substrate determines that it has completed one configuration. Using serial communication, the module on the substrate coordinates a rejection sequence with other module. At the same instant, both modules pulse their electromagnets at the same polarity and the second module returns to float about in the medium. Slowly, the dormant module is carried by the energy of the environment until it comes into a range where the magnetic force of the first module’s side causes the dormant module to attach. The experiment ends with the modules sitting in this reconfigured state. With Paul White, Victor Zykov

Construction Sequence High Pressure Low Pressure

Construction Sequence

Construction Sequence

Construction Sequence

Construction Sequence

Construction Sequence

Reconfiguration Sequence

Reconfiguration Sequence

Implementation 2: Fluidic Bonding [Viktor]: See for yourself. [Paul]: Wait, but this isn’t in the paper! [Viktor]: No, like any experimental work, we got it to work only a day before the conference. Throughout this demonstration, the central cube is attached to the substrate. It is programmed to attract the second freely floating module to its right-hand side, then release it and re-attract it to its upper side. This movie has been accelerated 16 times. In real time, the experiment completes in 10 minutes. [Paul]: Excuse me, how could you achieve 3D reconfiguration with only 2 cubes? [Viktor]: Thanks - Paul, that’s a good point. Accelerated x16 Real Time With Paul White, Victor Zykov

500 µm With David Erickson, Mike Tolley

Tile dimension: 500μm With Mike Tolley, Davis Erickson

Cytoskeleton of a mammalian cell Randomized Machines Tensegrity Robotics Particle Robotics Dictyostelium Don Ingber, Scientific American 1998 With Chandana Paul Cytoskeleton of a mammalian cell

Grand Challenges Can we design machines that can design other machines? Can we make machines that can make other machines? Can we make machines that can explain other machines? I’d like to conclude my talk today with what I consider to be two “grand challenges” of engineering. First, how can we design machines that can design other machines? And second, how can we make machines that can make other machines? Why are these challenges important? First, these two abilities are a uniting theme of engineering, and automating these processes will require us to address, in a very quantitative and algorithmic way, the underlying principles of our discipline. Second, making progress on these fronts will have huge leverage in many areas. Modern products are becoming increasingly complex, their complete design and fabrication often beyond the scope of understanding of any single person. Automating these processes will be an inevitable part of sustaining future progress. Finally, I’d like to say that Biological life has manages to address these two questions in ways that dwarf the best teams of engineers; It is therefore worth our while to look at nature: Not just to mimic the final results, but to truly understand the adaptive processes that led to their emergence, and harness these principles to our benefit. Thank you.