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

传统的设计和制造依赖于设计者的经验和训练来创建具有可制造特征的部件。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，包括某些特征可能显著增加制造成本。例如，包含薄特征可能需要使用复杂的夹具或固定装置来防止零件在机加工期间弯曲，这增加了制造时间和成本。在增材制造中也会遇到这个问题，其中没有关于将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。在复杂零件的制造中识别不可行性来源的过程是一项具有挑战性的任务，即使对于经验丰富的设计师来说也是如此。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性的来源来发挥关键作用。机器学习框架的结果将被用来建立一个决策支持框架，可以在设计过程中以交互方式识别可制造性问题，并以交互方式向设计师提出设计修改。最后，该项目的多学科组成部分将被整合到一个更大的教育努力，为学生提供一个坚实的基础，在关键的跨学科领域的网络使能制造。该项目的目标是创建一个设计的可制造性工具，使用机器学习来识别难以机加工或制造功能的计算机辅助设计模型，并建议改变不可制造的功能。这项研究的新奇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。研究团队将开发工具，用于加载现有的零件模型并进行虚拟加工模拟，以创建制造零件的数字体素化表示。原始的设计部件也将被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，并通过从正面和负面示例中学习来对可行和不可行的设计进行分类。此外，机器学习框架将用于向设计师呈现替代可行的设计。

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.