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Using Topology Optimization to Reduce Support Structures in Additive Manufacturing

使用拓扑优化来减少增材制造中的支撑结构

基本信息

批准号：
1561899
负责人：
Krishnan Suresh
金额：
$ 32.67万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-01 至 2020-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561899&HistoricalAwards=false
关键词：
Using Topology Optimization Reduce Support

项目摘要

Additive manufacturing represents a class of processes for fabricating parts of virtually any shape through material addition. The technique, often called 3D printing, offers several advantages over traditional manufacturing, and has the potential to revolutionize the way things are made. However, to fully exploit additive manufacturing, new design methods are needed. This award supports fundamental research towards developing a design framework for additive manufacturing. The design framework will not only consider traditional performance metrics but also additive manufacturing constraints. Specifically, the award will focus on the reduction of the sacrificial support structures needed to additively manufacture parts with geometric overhangs. By departing from conventional practice for support-structure design, the project's integrated approach will lead to low-cost products, benefitting the U.S. economy and society. Developing the framework involves several disciplines including design, manufacturing, and computer science. The research will help broaden participation of underrepresented groups and positively impact engineering education.This research will leverage topology optimization theory to integrate performance metrics and additive manufacturing constraints. For example, it will help minimize the compliance of the part, while respecting support structure constraints. The framework will be sufficiently general to accommodate a variety of performance metrics such as compliance, stress, and buckling, and a variety of additive manufacturing constraints such as support structures, surface roughness, and fabrication time. Robust level-set methods of topology optimization will be combined with augmented Lagrangian formulations to create a design framework that will then be integrated into these existing computer aided design systems and made available to the public. Experimental calibration and validation will be carried out to support the theory. The focus will largely be on polymer fused deposition modeling since this process is both inexpensive and easily accessible. However, experiments will also be carried out on metal additive manufacturing to establish the broader implications of the findings.

增材制造代表了通过材料添加制造几乎任何形状零件的一类工艺。这项技术通常被称为3D打印，与传统制造相比，它有几个优势，并有可能彻底改变产品的制造方式。然而，为了充分利用增材制造，需要新的设计方法。该奖项支持开发增材制造设计框架的基础研究。设计框架不仅要考虑传统的性能指标，还要考虑增材制造的限制。具体而言，该合同将侧重于减少增材制造具有几何悬垂的零件所需的牺牲支撑结构。与传统的支撑结构设计不同，该项目的综合方法将导致低成本产品，使美国经济和社会受益。开发该框架涉及多个学科，包括设计、制造和计算机科学。这项研究将有助于扩大代表性不足群体的参与，并对工程教育产生积极影响。本研究将利用拓扑优化理论来整合性能指标和增材制造约束。例如，它将有助于最小化零件的顺应性，同时尊重支撑结构的约束。该框架将足够通用，以适应各种性能指标，如顺应性、应力和屈曲，以及各种增材制造限制，如支撑结构、表面粗糙度和制造时间。鲁棒的水平集拓扑优化方法将与增强拉格朗日公式相结合，以创建一个设计框架，然后将其集成到这些现有的计算机辅助设计系统中，并向公众提供。将进行实验校准和验证以支持理论。重点将主要放在聚合物熔融沉积建模上，因为这种方法既便宜又容易获得。然而，还将对金属增材制造进行实验，以确定研究结果的更广泛影响。