The view of the backside of a digital knitting machine is beautiful in its chaos and fragility—and more than a little daunting. As one of seven students who traveled to Shenzhen, China, last August for Hacking Manufacturing, a course started by MIT Media Lab director Joi Ito, I got a firsthand look at that view—and a chance to see how a long-practiced manual skill like knitting translates to a piano-size machine.
In previous years, the class had toured many factories; last summer our three instructors decided we’d spend our four weeks in King Credie, which makes flexible printed circuit boards, and K-Tech, a digital knitting factory. Employees explained the processes in both facilities and gave us freedom to play with some of their machines. Our assignment: hacking a manufacturing process to make it more efficient or environmentally friendly, to introduce a new capability, or to create art.
I spent most of my time at K-Tech, where it was impressed upon me that knits are made with continuous stretches of yarn, woven together to stay firm and strong without breaking or fraying. Yet I was fascinated by the idea of tearing apart or breaking the result. On our first visit to K-Tech, we were shown different materials that could be used for knitting. For me, the most evocative demo involved a K-Tech staffer pouring boiling water into a cup and grinning as he tossed in a golf-ball-size wad of gray yarn. The yarn instantly dissolved.
K-Tech workers wanted to find a use for the dissolving yarn but didn’t have time to experiment. Since our reason for being there was to try new things, I tucked that idea away as we toured the factory, learned how to program a knit with visual software, and got a crash course in operating digital knitting machines. Soon we were programming our ideas and threading more than a dozen different yarns through a dizzying array of loops, holes, yarn carriers, and needles. Under the supervision of K-Tech staff—who, despite language barriers, swooped in to help when we struggled—we produced beautiful, colorful knits.
Things weren’t as easy when we were left on our own. We spent our first week experimenting, trying out yarns and patterns, and learning how to fix and clean the machines. We ruined so many needles it seemed we weren’t hacking manufacturing but breaking it. (Some students and K-Tech staffers even formed an impromptu band called “Broken Needles” and performed once at a bar.)
I quickly discovered that knitting holes or pockets is hard. If you make a hole big enough for a hand, the knit usually falls apart and deforms. On my own, I broke lots of needles, making ugly messes in the machine. When I asked K-Tech staffers for ideas, they lent me the software pattern for a shoe with pockets knit into the side. After experimenting, I adapted it to produce many, many pockets.
But all along, I thought about knitting to unravel with dissolving yarn. I experimented with knitting simple crisscrosses using polyester as the “criss” and dissolving yarn as the “cross.” The knit looked nice, but when I doused it with boiling water, it didn’t just unravel—it fell apart. I’d seen another student using a stiffening material called melting yarn to make rigid structures. So I mixed melting yarn with dissolving yarn and regular polyester to create patterns that resulted in mesh-like chain-mail structures.
Still, I kept thinking about unraveling. I wanted to create knits that came out whole but would dissolve into working components or parts. Grad student Jifei Ou, the instructor who led our trip, was trying to inlay copper wire into knits with a zigzag pattern. We used that pattern to knit zigzags of the dissolving yarn into other materials; pairing it with melting yarn let us break the resulting rectangle into triangles and other shapes.
Although we all learned different things in Shenzhen, we shared ideas and methodologies, mixing and matching yarns, patterns, and techniques to create new knits. And we came to understand that a knitting machine that can handle 16 yarns at once may outperform a two-handed human knitter, but it needs human attention and care. A give-and-take collaboration between human and machine produces the best result.
Grad student Laya Anasu is a research assistant in the Media Lab’s City Science group.