Computer scientists at the Carnegie Mellon University (CMU) in the United States have developed a system to translate a wide variety of 3-D shapes into stitch-by-stitch instructions that enable a computer-controlled knitting machine to automatically produce those shapes. The researchers have used the system produce a variety of plush toys and garments.
According to James McCann, assistant professor in the Robotics Institute and leader of the Carnegie Mellon Textiles Lab, this ability to generate knitting instructions without need for human expertise could make on-demand machine knitting possible, according to a press release from the university.Computer scientists at the Carnegie Mellon University (CMU) in the United States have developed a system to translate a wide variety of 3-D shapes into stitch-by-stitch instructions that enable a computer-controlled knitting machine to automatically produce those shapes. The researchers have used the system produce a variety of plush toys and garments.#
McCann's vision is to use the same machines that routinely crank out thousands of knitted hats, gloves and other apparel to produce customized pieces one at a time or in small quantities. Gloves, for instance, might be designed to precisely fit a customer's hands. Athletic shoe uppers, sweaters and hats might have unique colour patterns or ornamentation.
"Knitting machines could become as easy to use as 3-D printers," McCann said.
The team presented their findings in August at the SIGGRAPH 2018, the Conference on Computer Graphics and Interactive Techniques in Vancouver, Canada.
Widely-used machines manipulate loops of yarn with hook-shaped needles, which lie in parallel needle beds angled toward each other in an inverted V shape. The machines are highly capable, but are limited in comparison with hand knitting, said Vidya Narayanan, a PhD student in computer science at the university.
The CMU algorithm takes these constraints into account, she said, producing instructions for patterns that work within the limits of the machine and reduce the risk of yarn breaks or jams.
Other members in the team were Jessica Hodgins, professor of computer science and robotics, Lea Albaugh, a PhD student in the Human-Computer Interaction Institute, and Stelian Coros, a faculty member at ETH Zurich and an adjunct professor of robotics at CMU. (DS)
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