CHS: Medium: Collaborative Research: Computer-Aided Design and Fabrication for General-Purpose Knit Manufacturing

今天，工业针织机已经可以通用地制造复杂的3D形状，工业针织机提供了传统3D打印的强大替代方案，因为它们能够使用广泛的原料来生产坚固耐用的人工制品，可以承受极端的变形和拉伸。机器编织已经应用于从飞机部件的复合材料预制件、软机器人和医学到防弹衣、防剪手套和日常服装等功能服装。然而，控制工业针织机是一项困难的任务，因为它涉及确定产生所需3D形式的低级针织机指令的有效序列。该项目的目标是开发一个完整的框架，从计算机辅助设计到制造，用于通用针织制造，允许用户从任意3D形状开始，将该形状转换为虚拟针织结构，定制针织结构，并模拟结果的行为。项目成果将使通过机器编织的定制制造得到广泛应用，有可能改变制造实践，并在许多行业实现新产品和应用，包括机器人、家具、汽车、太空探索、医药、国防和服装。该项目的一个关键贡献将是在中尺度上进行模拟，使用常见图案的大型数据库来预览多针级别的行为。新的CAD/CAM工作流程中的关键步骤，包括将任意形状转换为编织结构，将建立在团队成员先前的工作基础上。该项目将涉及：使用高级和低级别操作定制编织结构；利用一种新的数据驱动、基于补丁的方法实时模拟编织结构，该方法利用从照片捕获的校准数据；低级别指令生成和验证，以包括允许(可选的)用户控制调度过程的基于提示的调度器。这项工作的主要贡献将是基于测量的模拟和管道的优化步骤，以及新的基于图像的纱线测量技术来支持它们。提示系统的集成和拓扑验证器的使用是创新，将赋予工作所需的灵活性和正确性保证，以启用拟议的管道。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

General-purpose fabrication of complex 3D shapes is possible today with industrial knitting machines, which provide a powerful alternative to conventional 3D printing, given their ability to use a wide range of feedstock to produce strong and durable artifacts that can withstand extreme deformations and stretch. Machine knitting is already used in applications ranging from composite preforms for aircraft parts, soft robotics, and medicine to functional clothing like body-armor, cut-proof gloves, and everyday apparel. However, controlling industrial knitting machines is a difficult task, as it involves determining an efficient sequence of low-level knitting machine instructions that produce a desired 3D form. The goal of this project is to develop a complete framework, from computer-aided design to fabrication, for general-purpose knit manufacturing that will allow users to start with an arbitrary 3D shape, convert the shape to a virtual knit structure, customize the knit structure, and simulate the behavior of the result. Project outcomes will enable widespread use of customized fabrication via machine knitting, with the potential to alter manufacturing practices and enable new products and applications in numerous industries, including robotics, furniture, automotive, space exploration, medicine, defense, and clothing. A key contribution of this project will be simulating at the meso-scale, using a large database of common patterns to preview behavior at the many-stitch level. Additional broad impacts will derive from seeding interdisciplinary cross-department and cross-college collaborations in both institutions.The key steps in the new CAD/CAM workflow, which involve converting arbitrary shapes to knit structures, will build on prior work by members of the team. The project will involve: customization of the knit structure using both high- and low-level operations; live simulation of the knit structure by exploiting a novel data-driven patch-based approach that draws on calibration data captured from photographs; and low-level instruction generation and verification to include a hint-based scheduler that allows (optional) user control of the scheduling process. Major contributions of this work will be the measurement-based simulation and the optimization steps of the pipeline, as well as the novel image-based yarn measurement technology to support them. The integration of a hinting system, and the use of a topological verifier, are innovations that will impart to the work the flexibility and guarantee of correctness required to enable the proposed pipeline.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.