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

I-Corps项目更广泛的影响/商业潜力是增材制造（AM）工程软件技术的发展。提出的技术可以通过实现各种智能评估、重新设计策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组件的打印准备情况，并自信地确定哪些组件可以可靠地使用增材制造工艺制造。这样的评估可以显著减少错误和打印失败，避免昂贵的试错练习，并消除人工评估过程中产生的偏差和不一致。此外，该技术还可以帮助设计师和制造商快速将有问题的现有组件几何形状转换为成功的3d打印设计，从而避免从头开始设计用于增材制造的新组件。所提出的技术通过将智能和自动化引入所需的评估中，为处理大批量生产的行业提供了价值，从而节省了大量的能源、计算和财务资源。I-Corps项目基于一种工程软件技术的开发，该技术集成了用于增材制造（AM）的多个智能评估和重新设计推荐系统。虽然许多行业越来越多地实施增材制造，但增材制造对于许多部件来说并不是一种可行的制造方法，实施不当可能会导致资源浪费和产品失效。目前的技术提出了一种多变量算法，用于可行性评估，在打印零件之前，从几个技术和经济角度监控组件的设计准备情况。该系统采用计算方法，智能地计算出代表各部件精确可行性状态的可行性分数。提出的增材制造技术提出了一种全自动校正系统，可以校正有问题组件的几何形状，从而实现增材制造的即时兼容性。提出的重新设计解决方案也可以为3d打印组件提供成功的几何形状，用户迭代最少，适用于不同的增材制造技术。此外，提出的技术提出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密切相关组件的广泛数据库，并为当前几何形状不可行的组件集群提供有效的重新设计建议。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.