AI-based Magnetic Levitation Platform for Smart Manufacturing

基于AI的智能制造磁悬浮平台

• 批准号: RGPIN-2022-03192
• 负责人: Khamesee, MirBehrad
• 金额: $ 1.97万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760426
• 关键词: AI; based; Magnetic; Levitation; Platform

Many manufacturing and assembly facilities need large spaces for transporting materials between working stations using traditional conveyor systems, and employ pick-and-place robots to position objects within each working station. This is the best current automation in industry where the global industrial automation market is projected to reach $306.2 Billion USD by 2027 (Meticulous Market Research Ltd., March 2021). Magnetic levitation (Maglev) technology, combined with artificial intelligence (AI), can revolutionize industrial automation. The current best practices will be significantly improved by the proposed research program to develop an AI-based magnetic levitation platform for smart manufacturing. The Maglev platform consists of arrays of electromagnetic coils hidden in a pit under the floor. The system can levitate multiple magnetic carriers at the same time, producing fast motions to independently transport objects with instant directional change, which traditional conveyor systems and wheeled carts are not able to achieve. The industrial automation market can benefit from faster productions, smaller factory spaces, and lower carbon footprints. This research program will make the following major contributions in fundamental sciences in robotics, mechatronics, Maglev, and electromechanical systems: a)As a long-term objective, a novel Maglev platform will be developed to anticipate the future needs in the ever-evolving smart manufacturing industry. This system design will be based on significant theoretical and finite element analyses, and electromagnetic systems design. b)Research on modeling of Maglev carriers for optimizing magnetic energy, reducing leakage flux with advanced magnetic circuit designs, and increasing the levitation gap which is a current drawback in using the Maglev systems. c)Research on AI and data driven techniques to enable levitation of multi-carriers to move rapidly without colliding, and minimizing energy consumption and lead time. d)Laser displacement sensors and Machine Learning will be applied to improve the vision system resolution, and to solve the problem of accidental camera vision blockage. This will have a significant impact in the robotic industry as AI vision systems for motion control are becoming increasingly popular. e)As a first-of-its-kind in a collaborative robotics theme, force-controlled collaborative manipulation will be realized using levitated multi-carriers to manipulate the same object to produce a larger payload capacity. Eleven trainees will be trained in mechatronics, robotics, Maglev, systems integration, and automation, i.e., a high-demand engineering expertise in Canada that is desperately in need of trained personnel. This research will contribute to the growth and advancement of the Canadian technology sectors in automotive, electronics, software, and manufacturing industries, and to the economic enhancement of Canada.

许多制造和组装设施都需要较大的空间来使用传统的传送带在工作站之间运输材料，并使用拾取和位置机器人将物体放置在每个工作站内。这是目前在行业中最好的自动化，到2027年，全球工业自动化市场预计将达到3062亿美元（Crevuly Market Research Ltd.，2021年3月）。磁悬浮（Maglev）技术，结合人工智能（AI），可以彻底改变工业自动化。拟议的研究计划将显着改善当前的最佳实践，以开发基于AI的智能制造的磁性悬浮平台。 Maglev平台由隐藏在地板下方的坑中的电磁线圈阵列组成。该系统可以同时悬浮多个磁性载体，从而产生快速的动作，以立即变化，传统的传送带系统和车轮手推车无法实现，从而独立运输对象。工业自动化市场可以从更快的生产，较小的工厂空间和较低的碳足迹中受益。该研究计划将在机器人技术，机械能力，磁机电系统中为基础科学提供以下重大贡献：a）作为一个长期目标，将开发一个新颖的Maglev平台，以预测不断增长的智能制造业的未来需求。该系统设计将基于重要的理论和有限元分析以及电磁系统设计。 b）研究岩石载体建模，以优化磁能，通过高级磁路设计减少泄漏通量以及增加悬浮缝隙，这是使用磁磁系统的当前缺点。 c）对AI和数据驱动技术的研究，以使多载体的悬浮能够迅速移动而无需碰撞，并最大程度地减少能耗和交付时间。 d）将应用激光位移传感器和机器学习来改善视觉系统的分辨率，并解决意外的摄像头视觉阻塞问题。这将对机器人行业产生重大影响，因为用于运动控制的AI视觉系统越来越流行。 e）作为协作机器人主题的首个，将使用悬浮的多载体来实现力控制的协作操作，以操纵相同的对象以产生更大的有效负载能力。 11名学员将接受机械能力，机器人技术，系统集成和自动化的培训，即加拿大的高需求工程专业知识，迫切需要训练有素的人员。这项研究将有助于加拿大技术领域的汽车，电子，软件和制造行业的增长和进步，并为加拿大的经济增强。