Integrated Framework for Cooperative 3D Printing: Uncertainty Quantification, Decision Models, and Algorithms

该奖项将资助通过推进数据分析和决策方法，提高新型合作3D打印（C3DP）技术的运行效率，为国家繁荣和经济福利做出贡献的研究。广泛采用增材制造（AM）技术的一个关键障碍是打印速度慢，导致大型部件的打印时间过长。C3DP利用一组携带打印头的移动机器人来协同执行打印作业，显著提高了可扩展性并缩短了打印时间。这些系统运行控制的有效方法必须考虑移动打印机的精度下降，这可能导致产品质量和生产效率的级联效应，以及生产过程中的不确定因素，不适合C3DP。本研究将通过提供创新的、集成的模型和算法来解决这些问题，以提高C3DP的运行效率。该项目还将通过为K-12、本科生和研究生提供多学科研究和培训机会，培养下一代科学家和工程师。多学科团队将结合增模、优化和随机模型，实现三个具体的研究目标：(1)建立先进的混合高阶隐马尔可夫模型，用于机器人打印机的位置精度预测和隐藏条件推断，便于机器人打印机的及时维护。(2)建立了一套基于动态机会约束的随机优化模型，用于维修计划、生产调度和无碰撞路径。(3)通过在研究实验室进行概念验证实验、计算模拟以及与工业合作伙伴的合作，验证和演示研究方法。将开发一个模拟器和C3DP平台来验证和演示这些方法。这些模型和算法的成功开发将有可能将增材制造转变为一个新的、超高效的自动化3D打印时代。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。

This award will fund research that contributes to national prosperity and economic welfare by advancing data analytics and decision-making methods for enhancing the operational efficiency of the novel cooperative 3D printing (C3DP) technology. A critical barrier to the widespread adoption of additive manufacturing (AM) technologies has been slow printing speeds, leading to excessive printing times for large parts. C3DP utilizes a fleet of printhead-carrying mobile robots to perform printing jobs cooperatively, significantly improving scalability and reducing print time. Effective methods for operational control of these systems must consider the accuracy degradation of mobile printers, which can lead to cascading effects in product quality and production efficiency, as well as uncertain factors in the production process and are unsuitable for C3DP. This research will address these issues by providing innovative, integrated models and algorithms to improve the operational efficiency of C3DP. This project will also prepare the next generation of scientists and engineers by providing multidisciplinary research and training opportunities for K-12, undergraduate, and graduate students.The multidisciplinary team will incorporate AM, optimization, and stochastic models to achieve three specific research objectives: (1) Create an advanced mixed higher-order hidden Markov model for positional accuracy prediction of robot printers and inference of hidden conditions, facilitating timely maintenance of robot printers. (2) Develop a suite of stochastic optimization models using dynamic chance constraints for maintenance planning, production scheduling, and collision-free routing. (3) Validate and demonstrate the research methods through proof-of-concept experiments at their research labs, computational simulations, and collaborations with industrial partners. A simulator and a C3DP platform will be developed to validate and demonstrate the methods. Successful development of these models and algorithms will potentially transform AM into a new, ultra-efficient era of automated 3D printing.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.