Hybrid intelligent system for Manufacturing (Hi4Man)

制造混合智能系统 (Hi4Man)

• 批准号: RGPIN-2017-04516
• 负责人: Ahmad, Rafiq
• 金额: $ 1.53万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711113
• 关键词: Hybrid; intelligent; system; Manufacturing; Hi4Man

Production technologies such as subtractive (machining) however, product and process complexity leads to other challenges such as manual setup changeovers, part dislocations, feature identification, operations scheduling, tracking of material deposition, perception of robots, and collisions avoidance. These challenges lead to increasing manufacturing costs, and limit the broad implementation of existing advanced manufacturing techniques. Thus, machines need intelligent, fast, and robust systems to enhance their performance and reconfigure themselves for unexpected/unavoidable changes. The proposed research program will overcome these limitations during hybrid manufacturing (HM) of combined additive & subtractive processes and robotics for enhanced accuracy, speed, quality, automation and intelligence. In this research program a methodologies based on knowledge from the design, initial plan, preparation and real manufacturing (vision sensors) will be developed. This will be implemented on a HM system to be designed and developed. Based on our developed snakes & ladders and level-set algorithms the deposition of materiel and manufacturing strategy will be generated. Neural network and supervised-learning rules will complement the decision process. The systems developed will then be implemented on a high-level platform using standardized ontologies. Finally, the developed systems will be extended to application of multi-pass welding and assembly automation in a human-robot collaborative environment. The program aims to advance state-of-the-art technologies in HM and robotics by integrating machining and additive manufacturing, effective re-manufacturing to enhance clean and green technologies, safe human-machine interaction and minimizing wastes in manufacturing. The production of such innovative hybrid systems, which is faster and more advanced than existing technologies on the market will certainly benefit Canadian manufacturing industries in order to match the predicted global 3D printing compound annual growth rate of 28.5% by 2022. In addition, the program will enable the training of at least ten HQPs on cutting edge technologies.

然而，诸如减法（加工）之类的生产技术会导致其他挑战，例如手动设置转换，部分位错，功能识别，操作计划，跟踪材料沉积，对机器人的感知和避免碰撞。这些挑战导致制造成本的增加，并限制了现有高级制造技术的广泛实施。因此，机器需要智能，快速和健壮的系统来增强其性能并重新配置出意外/不可避免的更改。拟议的研究计划将在混合制造（HM）中克服这些限制（HM），以增强精度，速度，质量，自动化和智能，以提高添加剂和减法过程和机器人技术。 在该研究计划中，将开发基于设计，初始计划，准备和实际制造（视觉传感器）的知识的方法。这将在设计和开发的HM系统上实施。根据我们发达的蛇和梯子和级别算法，将产生物料和制造策略的沉积。神经网络和监督学习规则将补充决策过程。然后，开发的系统将使用标准化的本体论在高级平台上实现。最后，开发的系统将扩展到在人类机器人协作环境中的多通焊接和组装自动化的应用。 该计划旨在通过整合加工和添加剂制造，有效的重新制造来提高HM和机器人技术的最新技术，以增强清洁和绿色的技术，安全的人机相互作用并最大程度地减少制造中的废物。这种创新的混合动力系统的生产比市场上的现有技术更快，更先进将使至少十个HQP在尖端技术上进行培训。