CM: Machine-Learning Driven Decision Support in Design for Manufacturability

CM：可制造性设计中机器学习驱动的决策支持

基本信息

批准号：
1644441
负责人：
Adarsh Krishnamurthy
金额：
$ 41.52万
依托单位：
Iowa State University
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：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1644441&HistoricalAwards=false
关键词：
CM Machine Learning Driven Decision

项目摘要

Traditional design and manufacturing relies on the experience and training of the designer to create a component with manufacturable features. However, even after careful design, the as-manufactured part might differ from the as-designed part. In addition, the inclusion of certain features might significantly increase the manufacturing cost. For example, the inclusion of a thin feature might necessitate the use of complex jigs or fixtures to prevent the flexing of the part during machining, which increases manufacturing time and cost. This problem is also encountered in additive manufacturing, where there is no body of knowledge regarding design rules that will reduce manufacturing defects. This project aims to address this challenge by developing computer-aided design tools that can identify difficult-to-manufacture features using machine learning. The process of identification of the source of infeasibility in manufacturing in a complex part is a challenging task, even for an experienced designer. Therefore, the use of machine learning could potentially play a critical role by detecting non-intuitive patterns from examples of feasible and infeasible parts, and identifying the source of infeasibility. The results of the machine-learning framework will be used to build a decision support framework that can interactively identify manufacturability concerns during the design process and present design modifications interactively to the designer. Finally, the multidisciplinary components of the project will be integrated into a larger educational effort to offer students a solid foundation in the critical interdisciplinary area of cyber-enabled manufacturing.The objective of this project is to create a design for manufacturability tool that uses machine learning to identify difficult to machine or manufacture features in a computer-aided design model and suggest changes to the non-manufacturable features. The novelty of this research is the use of machine learning in a computer-aided design and manufacturing environment, making it accessible to designers using a familiar design interface. The research team will develop tools for loading existing models of parts and performing virtual machining simulations to create a digital voxelized representation of the as-manufactured part. The original as-designed part will also be converted to a voxelized representation that will be suitable for machine learning. The machine-learning framework will be trained using multiple machining simulations and will classify feasible and infeasible designs by learning from positive and negative examples. Furthermore, the machine-learning framework will be used to present alternative feasible designs to the designer.

传统的设计和制造依赖于设计师的经验和培训来创建具有可制造特征的组件。然而，即使经过精心设计，制造的零件也可能与设计的零件不同。此外，包含某些功能可能会显着增加制造成本。例如，包含薄特征可能需要使用复杂的夹具或固定装置来防止零件在加工过程中弯曲，这会增加制造时间和成本。在增材制造中也会遇到这个问题，因为没有关于可减少制造缺陷的设计规则的知识体系。该项目旨在通过开发计算机辅助设计工具来应对这一挑战，这些工具可以使用机器学习识别难以制造的特征。即使对于经验丰富的设计师来说，识别复杂零件制造中的不可行性根源的过程也是一项具有挑战性的任务。因此，机器学习的使用可以通过从可行和不可行部分的示例中检测非直观模式并识别不可行的根源来发挥关键作用。机器学习框架的结果将用于构建决策支持框架，该框架可以在设计过程中交互式地识别可制造性问题，并向设计者交互式地呈现设计修改。最后，该项目的多学科组成部分将被整合到更大的教育工作中，为学生在网络化制造的关键跨学科领域打下坚实的基础。该项目的目标是创建一个使用机器学习的可制造性设计工具识别计算机辅助设计模型中难以加工或制造的特征，并建议对不可制造的特征进行更改。这项研究的新颖之处在于在计算机辅助设计和制造环境中使用机器学习，使设计人员可以使用熟悉的设计界面进行访问。研究团队将开发用于加载现有零件模型并执行虚拟加工模拟的工具，以创建制造零件的数字体素化表示。原始设计部分也将转换为适合机器学习的体素化表示。机器学习框架将使用多个加工模拟进行训练，并通过学习正例和反例来对可行和不可行的设计进行分类。此外，机器学习框架将用于向设计者提供替代的可行设计。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Scalable adaptive PDE solvers in arbitrary domains

任意域中的可扩展自适应 PDE 求解器

DOI：
10.1145/3458817.3476220
发表时间：
2021-11
期刊：
Storage and Analysis
影响因子：
0
作者：
Saurabh, Kumar;Ishii, Masado;Fernando, Milinda;Gao, Boshun;Tan, Kendrick;Hsu, Ming;Krishnamurthy, Adarsh;Sundar, Hari;Ganapathysubramanian, Baskar
通讯作者：
Ganapathysubramanian, Baskar

Direct 3D printing of multi-level voxel models

多层次体素模型的直接3D打印

DOI：
10.1016/j.addma.2021.101929
发表时间：
2021-04-01
期刊：
Additive manufacturing
影响因子：
11
作者：
Sambit Ghadai;Anushrut Jignasu;A. Krishnamurthy
通讯作者：
A. Krishnamurthy

Algorithmically-consistent deep learning frameworks for structural topology optimization

用于结构拓扑优化的算法一致的深度学习框架

DOI：
10.1016/j.engappai.2021.104483
发表时间：
2020-12-09
期刊：
Eng. Appl. Artif. Intell.
影响因子：
0
作者：
Jaydeep Rade;Aditya Balu;Ethan Herron;Jay Pathak;Rishikesh Ranade;S. Sarkar;A. Krishnamurthy
通讯作者：
A. Krishnamurthy

Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models

用于学习 CAD 模型中多尺度空间特征的多分辨率 3D CNN