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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：
1727190
负责人：
Sara Behdad
金额：
$ 29.24万
依托单位：
SUNY at Buffalo
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727190&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，因为它改变了产品的构思，设计，制造和分销给最终用户的方式。该研究项目解决了目前阻碍工业相关规模增材制造产品生产的关键科学和工程障碍。特别是，该项目的重点是如何更好地设计将通过增材制造生产的产品，考虑制造限制和与将零件组装成最终产品相关的挑战。该研究将产生用于生成和选择设计概念的快速算法，这些设计概念具有覆盖所有功能要求所需的最少数量的部件，并满足增材制造过程所施加的约束。这将促进3D打印产品的大批量生产，从而进一步提高增材制造的实用性和经济影响。该项目通过布法罗大学（UB）和罗切斯特理工学院（RIT）的共同努力跨越了几个学科。该项目将通过将这项研究的成果引入现有课程，加强两所参与大学与增材制造设计相关的课程。外联活动将通过与UB的技术精明计划合作组织的针对K-12学生的研讨会，促进妇女和少数民族的招聘。研究界将通过一个关于基于决策的设计中计算效率高的方法的路线图讲习班参与。本研究的目的是定义一个规定的理论框架和实用方法，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。该框架是基于功能需求的独立性原则和建模的物理集成问题的图着色问题。这项研究将导致新的，计算效率（多项式时间）图形着色算法的基础上放电技术结合组合零星。新的算法和设计框架将在代表性的设计问题上进行演示和评估，并通过增材制造实现最终设计。将考虑制造生产率措施，如表面粗糙度、建立时间和成本。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing

DOI：
10.1115/1.4046746
发表时间：
2020-06-01
期刊：
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
影响因子：
4
作者：
Huang, Jida;Sun, Hongyue;Xu, Wenyao
通讯作者：
Xu, Wenyao

An Optimal Quantity of Scheduling Model for Mass Customization-Based Additive Manufacturing

基于大规模定制的增材制造最优数量调度模型

DOI：
发表时间：
2019
期刊：
CA.
影响因子：
0
作者：
Oh, Yosep;Behdad, Sara
通讯作者：
Behdad, Sara

Part decomposition and 2D batch placement in single-machine additive manufacturing systems

DOI：
10.1016/j.jmsy.2018.07.006
发表时间：
2018-07
期刊：
Journal of Manufacturing Systems
影响因子：
12.1
作者：
Yosep Oh;Chi Zhou;S. Behdad
通讯作者：
Yosep Oh;Chi Zhou;S. Behdad

Part Separation Technique for Assembly-Based Design in Additive Manufacturing using Genetic Algorithm

DOI：
10.1016/j.promfg.2019.06.208
发表时间：
2019
期刊：
Procedia Manufacturing
影响因子：
0
作者：
A. Deka;S. Behdad
通讯作者：
A. Deka;S. Behdad

Sustainability and Life Cycle Product Design (Chapter 22)

可持续性和生命周期产品设计（第 22 章）

DOI：
10.1007/978-3-030-11866-2
发表时间：
2019
期刊：
Women in Industrial and Systems Engineering Key Advances and Perspectives on Emerging Topics
影响因子：
0
作者：
Thurston, Deborah;Behdad, Sara;Smith, Alice
通讯作者：
Smith, Alice