CAREER: Multiobjective Learning Control Strategies for Additive Manufacturing

该学院早期职业发展（Career）奖的研究目标是为增材制造的高通量和高精度控制创建系统理论设计和分析工具。尽管具有巨大的潜力，但由于工艺可靠性差，增材制造尚未在生产规模上交付功能部件。可靠性低的一个关键原因是缺乏精确的过程模型和开环过程控制。为了应对这一挑战，将通过学习算法利用增材制造独特的逐层材料沉积机制，该算法依赖于基于传感器数据和部分模型信息的迭代改进。受多目标优化的启发，提出的层对层学习控制算法将同时解决多个相互冲突的目标，以在存在建模误差和操作条件不确定性的情况下提供可接受的零件几何形状和机械性能。该项目的可交付成果包括(1)针对基于喷气打印和选择性金属熔化工艺的多目标学习控制算法的设计和分析工具，(2)在实验测试台上验证提出的算法，(3)开发K-12级增材制造和自动化的动手实验和互动课程模块，以及(4)培训本科生和研究生从事自动化，制造业，制造业和制造业的职业。和控制。该项目的成功完成将通过提高可靠性、可重复性和生产率，对快速扩张的数十亿美元的增材制造行业产生强烈的积极影响。具体来说，提高聚合物增材制造工艺的可靠性和性能可以对一次性大规模定制部件的制造产生重大影响，包括生物医学植入物、光机械部件和组织工程。另一方面，可靠的金属增材制造工艺可以为航空工业等安全关键应用生产零件。通过提出的推广和教育计划，该项目将传播对先进制造业重要性和机会的认识，培养学生的关键技能，并激励有才华的年轻工程师追求自动化，控制和先进制造业的职业生涯。

The research objective of this Faculty Early Career Development (CAREER) award is to create system-theoretic design and analysis tools for high-throughput and high-precision control of additive manufacturing. Despite their tremendous potential, additive manufacturing have not yet delivered functional parts at a production scale due to poor process reliability. A key reason for the low reliability is the lack of accurate process models coupled with open-loop process control. To counter this challenge, the unique layer-by-layer material deposition mechanism of additive manufacturing will be exploited by learning algorithms that rely on iterative refinement based on sensor data in conjunction with partial model information. Inspired by multi-objective optimization, the proposed layer-to-layer learning control algorithm will simultaneously address multiple conflicting objectives to deliver acceptable part geometry and mechanical properties, in the presence of modeling errors and uncertainties in operating conditions. Deliverable of this project include (1) design and analysis tools for multi-objective learning control algorithms tailored towards jet-based printing and selective metal melting processes, (2) verification of proposed algorithms on experimental test beds, (3) development of hands-on experimentation and interactive course modules on additive manufacturing and automation at the K-12 level, and (4) training of undergraduate and graduate students for careers in automation, manufacturing, and control. The successful completion of this project will make a strong positive impact on the rapidly expanding billion-dollar additive manufacturing industry by increasing reliability, repeatability, and production rate. Specifically, enhancing reliability and performance of polymer-based additive manufacturing process can have a significant impact on the fabrication of one-off mass-customized parts including biomedical implants, opto-mechanical components, and tissue engineering. On the other hand, reliable metal-based additive manufacturing processes can produce parts for safety-critical applications such as in aviation industry. Through the proposed outreach and education plan, this project will spread awareness about the importance and opportunities in advanced manufacturing, train students in critically needed skills, and inspire talented young engineers to pursue careers in automation, control, and advanced manufacturing.