ERI: An Artificial Intelligence-based Computer Aided Manufacturing Framework for Hybrid Manufacturing

这项工程研究启动(ERI)拨款支持在制造工艺规划自动化方面贡献新知识的研究，并促进先进制造、计算机科学、数学建模和几何推理领域的基础科学进步。混合制造将不同的制造过程集成到一个系统中，从而能够直接从原材料或库存材料创建随时可用的功能部件。将加法制造和减法制造两种先进制造工艺相结合，通过提供在三维空间中添加和移除材料的自由，潜在地释放了几乎全部的制造能力。这允许为各种应用实现复杂形状和功能的部件设计。然而，这种非凡的制造能力也给刀具路径规划和运动控制带来了前所未有的挑战，阻碍了混合制造的更广泛应用。该奖项支持基础研究，以探索和开发基于人工智能(AI)的方法，以促进混合制造过程中更智能、更好的计算机辅助制造(CAM)工具。该项目促进了对自动化制造刀具路径规划和控制的理解，并实现了任何几何形状的零件的面向目标的自主制造。这项研究推进了数字制造，增强了可持续性，并培训了未来的熟练劳动力，使美国经济和社会受益。该项目惠及航空航天、国防、医疗保健、能源、农业等多个行业。这项研究为先进的高自由度(即5轴或更多轴操作)混合制造过程提供了一种新的全自动化计算机辅助制造(CAM)框架。该框架利用最先进的人工智能(AI)算法进行计算机辅助设计(CAD)几何分析和CAM刀轨规划和控制。各种制造过程的通用模型和提供最佳解决方案的人工智能方法是本研究的主旨。该模型的数据格式允许对人工智能方法的内在支持。研究了建立在神经网络、进化算法和强化学习基础上的新型人工智能算法，以实现自动刀具路径规划。这项工作通过填补关于如何利用和扩展人类知识和生产数据以实现新的制造能力的知识空白，推进了先进制造中的知识库。研究团队计划将5轴铣削减法工艺和5轴材料挤压/定向能沉积型添加剂制造工艺相结合，探索AI-CAM框架从一到两再到多工艺的能力，并为AI-CAM建立标准的培训和测试方法，以进一步扩展和推广该框架。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。

This Engineering Research Initiation (ERI) grant supports research that contributes new knowledge in manufacturing process planning automation and promotes the progress of fundamental science in the fields of advanced manufacturing, computer science, mathematical modeling, and geometric reasoning. Hybrid manufacturing integrates different manufacturing processes in one system, enabling the creation of a ready-to-use functional part directly from raw or stock material. Hybridizing two advanced manufacturing processes, additive and subtractive manufacturing, potentially unleashes nearly full manufacturing capability by providing the freedom of adding and removing material in three-dimensional space. This permits the realization of part designs of complex shapes and functionality for a variety of applications. However, this extraordinary manufacturing capability also introduces unprecedented challenges in toolpath planning and motion control, impeding the broader application of hybrid manufacturing. This award supports fundamental research to explore and develop artificial intelligence (AI)-based methods to facilitate smarter and better computer aided manufacturing (CAM) tools for hybrid manufacturing processes. The project advances the understanding of automated manufacturing toolpath planning and control and enables goal-oriented autonomous fabrication of parts of any geometry. This research advances digital manufacturing, enhances sustainability, and trains the future skilled workforce, which benefits the U.S. economy and society. The project benefits several industries such as aerospace, defense, healthcare, energy, agriculture, and others. This research lays out a new fully automated computer-aided manufacturing (CAM) framework for advanced High-Degree-of-Freedom (i.e., 5 or more axes operation) hybrid manufacturing processes. This framework leverages state-of-the-art artificial intelligence (AI) algorithms for computer-aided design (CAD) geometry analysis and CAM toolpath planning and control. A generalized model for various manufacturing processes and the AI approach that provides the best solution is the thrust of this research. The data format of the model allows inherent support for AI methodology. New AI algorithms that are built on neural networks, evolutionary algorithms, and reinforcement learning are investigated for automated toolpath planning. This work advances the knowledge base in advanced manufacturing by filling the knowledge gap on how human knowledge and production data can be harnessed and extended to realize new manufacturing capabilities. The research team plans to hybridize a 5-axis milling subtractive process and a 5-axis material extrusion/directed energy deposition type additive manufacturing process, explore the AI-CAM framework’s capability to expand from one to two and then to multiple processes, and establish a standard training and testing methodology for AI-CAM for further expansion and generalization of the framework.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.