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

• 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

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部件。然而，即使经过仔细的设计，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著增加制造成本。例如，包含薄特征可能需要使用复杂的夹具或夹具，以防止零件在加工过程中发生弯曲，这会增加制造时间和成本。这个问题在加法制造中也会遇到，因为没有关于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来识别难以制造的特征。识别复杂零件制造中不可行的根源是一项具有挑战性的任务，即使对经验丰富的设计师来说也是如此。因此，机器学习的使用可能会发挥关键作用，从可行和不可行部分的例子中检测非直觉模式，并确定不可行的来源。机器学习框架的结果将被用来建立一个决策支持框架，该框架可以在设计过程中交互地确定可制造性问题，并以交互方式向设计者提出设计修改。最后，该项目的多学科组成部分将被整合到更大的教育努力中，为学生在网络制造的关键跨学科领域提供坚实的基础。该项目的目标是创建一种可制造性设计工具，该工具使用机器学习来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。这项研究的新颖性在于在计算机辅助设计和制造环境中使用机器学习，使设计师可以使用熟悉的设计界面访问机器学习。研究团队将开发用于加载现有零件模型和执行虚拟加工模拟的工具，以创建制造零件的数字体素表示。原来设计的零件也将转换为适合机器学习的体素表示法。机器学习框架将使用多个加工模拟进行训练，并将通过从正反两个例子中学习来对可行和不可行的设计进行分类。此外，机器学习框架将被用来向设计者呈现替代的可行设计。

项目成果

Direct 3D printing of multi-level voxel models

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

Algorithmically-consistent deep learning frameworks for structural topology optimization

• DOI: 10.1016/j.engappai.2021.104483
• 发表时间: 2021-10-09
• 期刊: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
• 影响因子: 8
• 作者: Rade, Jaydeep;Balu, Aditya;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh

Multi-Level 3D CNN for Learning Multi-Scale Spatial Features

• DOI: 10.1109/cvprw.2019.00150
• 发表时间: 2018-05
• 期刊: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 作者: Sambit Ghadai;Xian Yeow Lee;Aditya Balu;S. Sarkar;A. Krishnamurthy

NURBS-Python: An open-source object-oriented NURBS modeling framework in Python

• DOI: 10.1016/j.softx.2018.12.005
• 发表时间: 2019-01-01
• 期刊: SOFTWAREX
• 影响因子: 3.4
• 作者: Bingol, Onur Rauf;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh

Orthogonal Distance Fields Representation for Machine-Learning Based Manufacturability Analysis

基于机器学习的可制造性分析的正交距离场表示

• DOI: 10.1115/detc2020-22487
• 发表时间: 2020
• 期刊: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE
• 作者: Balu, Aditya;Ghadai, Sambit;Sarkar, Soumik;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh