CHS: Small: Pattern Understanding and Computational Modeling for Textiles

纺织生产是一个丰富的制造领域，受到几代人创造力的影响。然而，数字设计工具的缺乏造成了社区创造力和现代制造技术之间的差距。有机会从创作者分享的大量在线模式库中学习，可以提供一个创造性思维的新来源。这个项目将开发新的技术来理解和编码手工制作的模式在其所有的复杂性，并支持修改它们的方式是机器或手工生产。这反过来又可以帮助支持模式的定制和个性化，以满足特定环境的需求，从软机器人到适合所有体型的模式。在这项工作中开发的数字化设计数据库将作为未来工作的资源，经验分析在线社区开发的设计空间，并支持在新的设计工具中应用深度学习技术。这项研究将建立特定领域的语言和工具，用于表示，学习，操作，验证和自动制造手工制作的纺织品图案，并支持直接从这组丰富的在线数据中进行数字化的最终目标。第一个任务是定义一个高级的领域特定语言以及一个较低级别的基于图形的模型，表示针脚和服装组件之间的连接，解析器和编译器之间的转换良好形成的模式和这个图形表示，和工具，以验证模式的正确性机器和/或手工生产。第二个任务是开发一种混合的人/机方法，用于解析目前在线数据库上可用的大量模式。可视化工具将在股线一级的物理模拟的基础上开发，使用户能够理解，修改和验证他们的模式。最后，本研究将探讨新的生成设计技术，以支持修改图案纹理和形状，以及新的形状或形状元素添加到现有的服装建设。这些将通过开发优化技术来实现，其目标是总体形状和精细纹理，在针脚的离散空间上，并在可制造性约束下。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Textile production is a rich domain of fabrication, influenced by generations of creativity. However, the lack of digital design tools creates a gap between the community's creativity and modern manufacturing techniques. The opportunity to learn from massive online repositories of patterns shared by creators could provide access to a novel source of creative ideation. This project will develop new techniques to understand and encode hand-produced patterns in all their complexity, and support modifying them in ways that are machine- or hand-producible. This in turn could help to support customization and personalization of patterns to meet the needs of specific contexts from soft robotics to accessibility to patterns that fit all body shapes. The database of digitized designs developed during this work will serve as a resource for future work empirically analyzing the space of designs that online communities have developed, and support applying deep learning techniques in new design tools. This research will establish the domain-specific languages and tools necessary to represent, learn from, manipulate, verify, and automatically fabricate hand-produced textile patterns and support the eventual goal of digitizing directly from this rich set of online data. The first task is to define a high-level domain specific language as well as a lower-level graph-based model that represents the connections between stitches and components of a garment, a parser and compiler for translating between well formed patterns and this graph representation, and tools to verify pattern correctness for machine and/or hand production. The second task is to develop a hybrid human/machine method for parsing the plethora of patterns currently available on online databases. Visualization tools will be developed based on physical simulation at the strand level to allow users to understand, modify, and verify their patterns. Finally, this research will explore new generative design techniques to support modification of pattern texture and shape as well as construction of novel shapes or additions of shaped elements to existing garments. These will be accomplished by developing optimization techniques with objectives on the gross shape and fine texture, over the discrete space of stitches, and under manufacturability constraints.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.