Collaborative Research: HCC: Medium: Co-Design of Shape and Fabrication Plans for Direct-Ink Write Printing Through Predictive Simulation
Collaborative Research: HCC: Medium: Co-Design of Shape and Fabrication Plans for Direct-Ink Write Printing Through Predictive Simulation
批准号:
2212048
负责人:
Paul Vouga
金额:
$39.92万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-10-01 至 2025-09-30
中文摘要
基于挤压的3D打印正在成为航空航天、汽车、医疗和国防应用中的重要工具,所有这些应用都需要对其组件拥有最高的信心。随着3D打印零件用于缓冲、减震以及传感器和执行器集成的使用越来越多,正确考虑和塑造零件制造方式的设计工具的重要性从未像现在这样明显。尽管有这种需求,但目前的设计工具只关注零件的外部几何形状,而忽略了打印过程本身。换句话说,尽管打印过程对最终零件的机械性能有关键影响,但重点是打印的内容,而不是打印的方式,例如打印机喷嘴在打印零件的每一层时所采用的路径。这项研究将改变3D打印部件的设计,首次允许用户不仅设计部件的形状,还设计其功能。该项目开发的新算法将自动从用户指定的机械行为映射到制造计划,该制造计划指定打印机应如何制造零件的低级别细节,以实现这些目标。由于制造业应用程序以一种可访问的、有形的方式激发学生对STEM的兴趣,该项目将产生额外的广泛影响,围绕制造业和STEM职业之间的关系开发教育材料,并通过建立与当地外展工作的联系来分享项目成果。这项工作将通过为基于直接墨水写入(DIW)技术的3D打印机创建用于共同设计物体的宏观形状和微观制造计划的新算法来弥合“形式-功能设计差距”。将开发一种基于微观结构的杆模拟,以准确地分析DIW印刷零件的力学行为,比传统的有限元方法高出一个数量级。通过允许制造计划的持续变化,本项目中定义的新的中间表示将为添加制造的新的交互式搜索和离线优化策略打开大门。最后,研究将开发一种新颖的双层优化策略,该策略可以联合设计物体几何形状和制造方案,同时解决每个几何设计定义不同的可能制造方案的挑战。在这项工作中开发的算法将针对实验室中的机械实验和模拟基准进行彻底测试。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。
英文摘要
Extrusion-based 3D printing is becoming an important tool in aerospace, automotive, medical, and defense applications, all of which require the highest confidence in their components. With the growth in the use of 3D-printed parts designed for cushioning, impact absorption, and integration of sensors and actuators, the importance of design tools that correctly account for and shape how a part is fabricated has never been more pronounced. Despite this need, current design tools focus exclusively on the part's outer geometric shape and ignore the printing process itself. In other words, the focus is on what is being printed rather than on how it's being printed, such as the path the printer nozzle takes when printing each layer of the part, despite the crucial effect the printing process has on the mechanical properties of the final part. This research will transform the design of 3D-printed parts by allowing users, for the first time, to design not only the part's shape but also its function. New algorithms developed by this project will automatically map from user-specified mechanical behavior to a Fabrication Plan that specifies the low-level details of how the printer should fabricate the part in order to achieve those goals. Since manufacturing applications excite students about STEM in an accessible, tangible way, this project will have additional broad impact by developing educational materials around the relationship between manufacturing and STEM careers, and by sharing project outcomes through established connections to local outreach efforts.This work will close the "form-function design gap" by creating new algorithms for co-designing both an object's macroscale shape and microscale fabrication plan, for 3D printers based on Direct-Ink-Write (DIW) technology. A microstructure-aware rod-based simulation will be developed to accurately analyze mechanical behavior of DIW-printed parts an order of magnitude more efficiently than traditional finite element methods. By allowing continuous variation of fabrication plans, the novel intermediate representation defined in this project will open the door for new interactive search and offline optimization strategies for additive manufacturing. Finally, the research will develop a novel bi-level optimization strategy that can jointly design object geometry and fabrication plan, while solving the challenge that each geometric design defines a different manifold of possible fabrication plans. The algorithms developed in this effort will be thoroughly tested against both mechanical experiments in the lab and simulated benchmarks.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.
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负责人:Paul Vouga
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依托单位:
国内基金
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