CHS: Small: Pattern Understanding and Computational Modeling for Textiles

纺织品生产是一个丰富的制造领域，受到几代人的创造力的影响。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。从创作者共享的大量在线模式存储库中学习的机会可以提供访问创造性想法的新来源。该项目将开发新的技术来理解和编码所有复杂的手工制作模式，并支持以机器或手工生产的方式修改它们。这反过来又有助于支持模式的定制和个性化，以满足从软机器人到适合所有体型的模式的可访问性等特定上下文的需求。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，对在线社区开发的设计空间进行实证分析，并支持在新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，以表示、学习、操作、验证和自动制作手工生产的纺织品图案，并支持直接从这一丰富的在线数据集进行数字化的最终目标。第一个任务是定义高级领域特定语言，以及低级基于图形的模型，该模型表示服装的缝线和组件之间的连接，用于在格式良好的模式和该图形表示之间进行翻译的解析器和编译器，以及用于验证机器和/或手工生产的模式正确性的工具。第二个任务是开发一种人/机器混合方法，用于解析在线数据库中当前可用的大量模式。可视化工具将基于链级的物理模拟来开发，以允许用户理解、修改和验证他们的模式。最后，本研究将探索新的生成设计技术，以支持图案纹理和形状的修改，以及新形状的构建或在现有服装上添加形状元素。这些将通过开发优化技术来完成，其目标是在总体形状和精细纹理上，在针迹的离散空间上，并在可制造性约束下。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.