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

ERI:基于人工智能的混合制造计算机辅助制造框架

基本信息

  • 批准号:
    2301725
  • 负责人:
  • 金额:
    $ 19.86万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-01 至 2025-07-31
  • 项目状态:
    未结题

项目摘要

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.
该工程研究启动(ERI)资助支持在制造过程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的进步。混合制造将不同的制造工艺集成在一个系统中,从而能够直接从原材料或库存材料中创建即用型功能部件。混合两种先进的制造工艺,增材制造和减材制造,通过提供在三维空间中添加和移除材料的自由度,可能释放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在刀具路径规划和运动控制方面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,以促进更智能,更好的计算机辅助制造(CAM)工具,用于混合制造过程。该项目推进了对自动化制造刀具路径规划和控制的理解,并实现了以目标为导向的任何几何形状零件的自主制造。这项研究推进了数字化制造,增强了可持续性,并培养了未来的熟练劳动力,这有利于美国经济和社会。该项目使航空航天、国防、医疗保健、能源、农业等多个行业受益。这项研究提出了一种新的全自动计算机辅助制造(CAM)框架,用于高级高自由度(即,5轴或更多轴操作)混合制造工艺。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刀具路径规划和控制。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重点。该模型的数据格式允许对AI方法的内在支持。新的人工智能算法,建立在神经网络,进化算法和强化学习的研究自动刀具路径规划。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,推进了先进制造业的知识基础。研究团队计划将5轴铣削减材工艺和5轴材料挤压/定向能量沉积型增材制造工艺混合,探索AI-CAM框架从一个扩展到两个再扩展到多个工艺的能力,并为人工智能建立标准的培训和测试方法,该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

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Niechen Chen其他文献

A new boundary interlock geometry design pattern to strengthen FDM part multi-material interface
  • DOI:
    10.1016/j.mfglet.2022.07.082
  • 发表时间:
    2022-09-01
  • 期刊:
  • 影响因子:
  • 作者:
    Shivaram Kakaraparthi;Robert A. Tatara;Niechen Chen
  • 通讯作者:
    Niechen Chen
A multi-material additive manufacturing virtual prototyping method for design to improve part strength

Niechen Chen的其他文献

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