Garment Design Workflows for On-Demand Machine Knitting
Modern computerized weft knitting machines enable on-demand production of custom, whole garments at once. They reduce the need for manual post-processing and generate minimal waste. Yet, their programming is still hardly accessible and is effectively done manually by few skillful knitting technician...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143409 https://orcid.org/0000-0002-6090-5392 |
| _version_ | 1826199916083412992 |
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| author | Kaspar, Alexandre |
| author2 | Matusik, Wojciech |
| author_facet | Matusik, Wojciech Kaspar, Alexandre |
| author_sort | Kaspar, Alexandre |
| collection | MIT |
| description | Modern computerized weft knitting machines enable on-demand production of custom, whole garments at once. They reduce the need for manual post-processing and generate minimal waste. Yet, their programming is still hardly accessible and is effectively done manually by few skillful knitting technicians. The programming of knitted garments typically involves scheduling hundreds of thousands of stitches. While every individual stitch created on such machines can, in theory, be controlled digitally, the ability to effectively do so depends heavily on the programming software being sufficiently accessible to the user. Unfortunately, current knitting software is typically closed and relies mostly on low-level programming. The lack of standardization and more accessible, higher-level user design tools effectively hinder the possibility of a digital, on-demand production of garments for all.
In this thesis, I explore the design space that flat-bed, weft knitting machines span and propose novel design workflows to enable accessible, digital customization of garments created on these machines. First, I introduce the inverse design problem of automatic knitting program generation from a single image, together with a machine learning framework that enables it. Second, I describe a parametric, primitive-based design tool that merges inspirations from both computer-aided design and pixel-based image editing. Finally, I propose a novel workflow to translate traditional, sketch-based garment patterns into knitting programs. The resulting system allows anyone to harness the plethora of existing garment designs while providing knitting-specific customization capabilities. |
| first_indexed | 2024-09-23T11:27:50Z |
| format | Thesis |
| id | mit-1721.1/143409 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:27:50Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1434092022-06-16T03:35:22Z Garment Design Workflows for On-Demand Machine Knitting Kaspar, Alexandre Matusik, Wojciech Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Modern computerized weft knitting machines enable on-demand production of custom, whole garments at once. They reduce the need for manual post-processing and generate minimal waste. Yet, their programming is still hardly accessible and is effectively done manually by few skillful knitting technicians. The programming of knitted garments typically involves scheduling hundreds of thousands of stitches. While every individual stitch created on such machines can, in theory, be controlled digitally, the ability to effectively do so depends heavily on the programming software being sufficiently accessible to the user. Unfortunately, current knitting software is typically closed and relies mostly on low-level programming. The lack of standardization and more accessible, higher-level user design tools effectively hinder the possibility of a digital, on-demand production of garments for all. In this thesis, I explore the design space that flat-bed, weft knitting machines span and propose novel design workflows to enable accessible, digital customization of garments created on these machines. First, I introduce the inverse design problem of automatic knitting program generation from a single image, together with a machine learning framework that enables it. Second, I describe a parametric, primitive-based design tool that merges inspirations from both computer-aided design and pixel-based image editing. Finally, I propose a novel workflow to translate traditional, sketch-based garment patterns into knitting programs. The resulting system allows anyone to harness the plethora of existing garment designs while providing knitting-specific customization capabilities. Ph.D. 2022-06-15T13:18:47Z 2022-06-15T13:18:47Z 2022-02 2022-03-04T20:48:00.917Z Thesis https://hdl.handle.net/1721.1/143409 https://orcid.org/0000-0002-6090-5392 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Kaspar, Alexandre Garment Design Workflows for On-Demand Machine Knitting |
| title | Garment Design Workflows for On-Demand Machine Knitting |
| title_full | Garment Design Workflows for On-Demand Machine Knitting |
| title_fullStr | Garment Design Workflows for On-Demand Machine Knitting |
| title_full_unstemmed | Garment Design Workflows for On-Demand Machine Knitting |
| title_short | Garment Design Workflows for On-Demand Machine Knitting |
| title_sort | garment design workflows for on demand machine knitting |
| url | https://hdl.handle.net/1721.1/143409 https://orcid.org/0000-0002-6090-5392 |
| work_keys_str_mv | AT kasparalexandre garmentdesignworkflowsforondemandmachineknitting |