I-Corps: An Intelligent Re-Design Recommender System for Additive and Hybrid Manufacturing

这个i-Corps项目更广泛的影响/商业潜力是开发用于添加剂制造(AM)的工程软件技术。建议的技术可用于通过实施各种智能评估、重新设计战略和学习过程来帮助解决一些重大挑战。它提供了一种方法来全面评估部件的打印准备情况，并自信地确定哪些部件可以使用AM工艺可靠地制造。这种评价可以大大减少错误和印刷失败，避免昂贵的反复试验，并消除人工评价过程中产生的偏见和不一致之处。此外，建议的技术有助于设计者和制造商将有问题的现有组件几何形状快速转换为成功的3D可打印设计，从而避免从头开始为AM制造设计新组件。这项拟议的技术通过将智能和自动化引入所需的评估以节省大量能源、计算和财务资源，为处理大批量生产的行业提供了价值。该i-Corps项目基于一种工程软件技术的开发，该技术集成了多个用于添加剂制造(AM)的智能评估和重新设计推荐系统。虽然越来越多的行业正在实施AM，但AM对于许多零部件来说并不是一种可行的制造方法，如果实施不当，可能会造成资源浪费和产品失败。目前的技术提出了一种用于可行性评估的多变量算法，该算法在打印部件之前从几个技术和经济角度监控部件的设计就绪情况。使用计算方法，该系统智能地计算出代表各个部件的精确可行性状态的可行性分数。拟议的AM技术提供了一个全自动校正系统，可以校正有问题的部件的几何形状，从而实现即时的AM兼容性。建议的重新设计解决方案还可以以最少的用户迭代为3D打印组件和不同的AM技术提供成功的几何形状。此外，拟议的技术提供了一个设计推荐系统，该系统使用机器学习技术智能地分析AM的密切相关组件的广泛数据库，并为当前几何形状是AM不可行的组件集群提供有效的重新设计建议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of engineering software technology for additive manufacturing (AM). The proposed technology may be used to help solve some of the significant challenges by implementing a variety of intelligent assessments, re-design strategies, and learning processes. It provides a way to evaluate comprehensively the print-readiness of components and confidently identify which components may reliably be manufactured using AM processes. Such evaluations may significantly reduce mistakes and printing failures, avoid costly trial-and-error exercises, and remove biases and inconsistencies that arise from manual evaluation processes. In addition, the proposed technology assists designers and manufacturers in rapidly converting problematic existing component geometries to successful 3D-printable designs, avoiding the need to design new components for AM fabrication from scratch. The proposed technology presents a value for industries dealing with high-volume production by introducing intelligence and automation into the required assessments to save significant energy, computational, and financial resources.This I-Corps project is based on the development of an engineering software technology integrating multiple intelligent assessment and re-design recommender systems for additive manufacturing (AM). While numerous industries are increasingly implementing AM, AM is not a feasible manufacturing method for many components, and improper implementation may cause resource waste and product failure. The current technology presents a multi-variable algorithm for feasibility evaluation that monitors the design-readiness of components from several technical and economic perspectives before printing the part. Using computational approaches, this system intelligently calculates feasibility scores representing the precise feasibility status of various parts. The proposed AM technology presents a fully automated correction system that rectifies the geometries of problematic components, allowing for immediate AM compatibility. The proposed re-design solutions also may provide successful geometries for 3D-printing components with minimal user-iteration and for different AM techniques. In addition, the proposed technology presents a design recommender system that intelligently analyzes an extensive database of closely related components for AM using machine learning techniques and provides effective re-design recommendations for component clusters whose current geometries are AM infeasible.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.