DeepKnit: Learning-based generation of machine knitting code
Name
1-s2.0-S2351978920319259-main.pdf
Description
Published version
Size
1.19 MB
Format
Adobe PDF
Checksum (MD5)
edbe31409d38a34426c15ed9db07d679
Author(s)
Scheidt, F
Ou, J
Ishii, H
Meisen, T
Date Issued
2020
Journal
Procedia Manufacturing
Publisher
Elsevier BV
Citation
Scheidt, F, Ou, J, Ishii, H and Meisen, T. 2020. "DeepKnit: Learning-based generation of machine knitting code." Procedia Manufacturing, 51.
Version
Final published version
Abstract
Modern knitting machines allow the manufacturing of various textile products with complex surface structures and patterns. However, programming these machines requires expert knowledge due to constraints of the process and the programming language. We present a long short-term memory (LSTM) based deep learning model that generates low-level code of novel knitting patterns based on high-level style specifications. To be processable by our model, we describe knitting instructions as one-dimensional sequences of tokens, which diverts from image-based approaches reported in previous research. We integrate our model into a design tool, that allows to assemble the atomic patterns to bigger swatches or garments. To evaluate our approach quantitatively, we formalize the requirements for patterns to be syntactically correct and valid to manufacture. Although our generated patterns look more random and seem to resemble less to human patterns, our evaluation shows that their knittability is orders of magnitudes better than randomly generated patterns.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.promfg.2020.10.068
