Collaborative Research: Improving Design for Additive Manufacturing through Physically-integrated Design Concepts Generated from Computationally Efficient Graph Coloring Techniques

协作研究：通过计算高效的图形着色技术生成的物理集成设计概念改进增材制造的设计

基本信息

批准号：
2017968
负责人：
Sara Behdad
金额：
$ 15.14万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017968&HistoricalAwards=false
关键词：
Collaborative Research Improving Design Additive

项目摘要

Additive manufacturing or 3D printing has been hailed as the third industrial revolution because of the unique way it changes how products are conceived, designed, manufactured and distributed to end users. This research project addresses key scientific and engineering barriers that currently prevent the production of additively manufactured products at an industrially-relevant scale. In particular, the project focuses on how to better design products that will be produced via additive manufacturing by considering manufacturing constraints and challenges associated with assembling parts into final products. The study will result in fast algorithms for generating and selecting design concepts that have the minimum number of parts required to cover all functional requirements and that meet constraints imposed by the additive manufacturing process. This will facilitate high-volume, mass production of 3D printed products, thereby furthering the practicality and economic impact of additive manufacturing. The project spans several disciplines through a joint effort between the University at Buffalo (UB) and Rochester Institute of Technology (RIT). The project will enhance the curriculum related to design for additive manufacturing at the two participating universities by introducing results from this research into existing courses. Outreach activities will promote the recruitment of women and minorities to advanced manufacturing through workshops aimed at K-12 students organized in partnership with the Tech-Savvy program at UB. The research community will be engaged through a road-mapping workshop on computationally efficient methods in decision-based design. The objective of this research is to define a prescribed theoretical framework and practical methods for classifying and generating physically integrated design concepts that can be realized effectively through additive manufacturing. The framework is based on the independence of functional requirements principle and modeling of physical integration problems as graph coloring problems. The research will result in new, computationally efficient (polynomial-time) graph-coloring algorithms based on discharging techniques in combination with Combinatorial Nullstellensatz. The new algorithms and design framework will be demonstrated and evaluated on representative design problems with the resulting designs being realized through additive manufacturing. Manufacturing productivity measures such as surface roughness, buildup time and cost, will be considered.

增材制造或 3D 打印被誉为第三次工业革命，因为它以独特的方式改变了产品的构思、设计、制造和分发给最终用户的方式。该研究项目解决了目前阻碍增材制造产品在工业相关规模生产的关键科学和工程障碍。该项目特别关注如何通过考虑与将零件组装成最终产品相关的制造限制和挑战，更好地设计通过增材制造生产的产品。该研究将产生用于生成和选择设计概念的快速算法，这些设计概念具有满足所有功能要求所需的最少零件数量，并且满足增材制造工艺施加的限制。这将促进 3D 打印产品的大批量、大规模生产，从而进一步提高增材制造的实用性和经济影响。该项目由布法罗大学 (UB) 和罗彻斯特理工学院 (RIT) 共同努力，跨越多个学科。该项目将通过将该研究的结果引入现有课程，加强两所参与大学与增材制造设计相关的课程。外展活动将通过与布法罗大学技术精通项目合作举办的针对 K-12 学生的讲习班，促进招募女性和少数族裔进入先进制造业。研究界将通过路线图研讨会参与基于决策的设计中计算有效的方法。本研究的目的是定义一个规定的理论框架和实践方法，用于分类和生成可以通过增材制造有效实现的物理集成设计概念。该框架基于功能需求独立性原则和将物理集成问题建模为图形着色问题。该研究将基于放电技术与组合 Nullstellensatz 相结合，产生新的、计算高效（多项式时间）的图形着色算法。新的算法和设计框架将针对代表性设计问题进行演示和评估，并通过增材制造实现最终的设计。将考虑表面粗糙度、成型时间和成本等制造生产力指标。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The impact of build orientation policies on the completion time in two-dimensional irregular packing for additive manufacturing

构建方向策略对增材制造二维不规则包装完成时间的影响

DOI：
10.1080/00207543.2019.1683253
发表时间：
2019-10
期刊：
International journal of production research
影响因子：
9.2
作者：
Oh, Yosep;Zhou, Chi;Behdad, Sara
通讯作者：
Behdad, Sara

Machine Learning to Predict Medical Devices Repair and Maintenance Needs

机器学习预测医疗设备维修和维护需求

DOI：
10.1115/detc2021-71333
发表时间：
2021-08
期刊：
the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2021
影响因子：
0
作者：
Liao, Hao;Boregowda, Karthik;Cade, Willie;Behdad, Sara
通讯作者：
Behdad, Sara

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

使用支持向量机预测医疗设备的维修和保养服务

DOI：
10.1115/msec2021-63966
发表时间：
2021-06
期刊：
the ASME International Manufacturing Science and Engineering Conference (MSEC
影响因子：
0
作者：
Liao, Hao;Cade, Willie;Behdad, Sara
通讯作者：
Behdad, Sara

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

使用支持向量机预测医疗设备的维修和保养服务

DOI：
10.1115/msec2021-63966
发表时间：
2021-06-21
期刊：
Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation
影响因子：
0
作者：
Hao;Willie Cade;S. Behdad
通讯作者：
S. Behdad

A Graph Partitioning Technique to Optimize the Physical Integration of Functional Requirements for Axiomatic Design

一种优化公理化设计功能需求物理集成的图划分技术

DOI：
10.1115/1.4052702
发表时间：
2022-05
期刊：
Journal of Mechanical Design
影响因子：
3.3
作者：
Green, Emilyn;Estrada, Spenser;Gopalakrishnan, Praveen Kumare;Jahanbekam, Sogol;Behdad, Sara
通讯作者：
Behdad, Sara