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: $ 3.06万
• 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: 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=760124
• 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，减材（加工）等生产技术、产品和工艺的复杂性会带来其他挑战，例如手动设置转换、零件错位、特征识别、操作调度、材料沉积跟踪、机器人感知和避免碰撞。这些挑战导致制造成本增加，并限制了现有先进制造技术的广泛实施。因此，机器需要智能、快速且强大的系统来增强其性能并重新配置自身以应对意外/不可避免的变化。拟议的研究计划将克服增减工艺和机器人技术的混合制造（HM）过程中的这些限制，以提高准确性、速度、质量、自动化和智能。在该研究项目中，将开发一种基于设计、初始计划、准备和实际制造（视觉传感器）知识的方法。这将在要设计和开发的 HM 系统上实施。基于我们开发的蛇梯和水平集算法，将生成材料的沉积和制造策略。神经网络和监督学习规则将补充决策过程。然后，开发的系统将在使用标准化本体的高级平台上实施。最后，开发的系统将扩展到人机协作环境中多道焊接和装配自动化的应用。该计划旨在通过集成机械加工和增材制造、有效的再制造以增强清洁和绿色技术、安全的人机交互以及最大限度地减少制造中的浪费，推进HM和机器人领域的最先进技术。这种比市场上现有技术更快、更先进的创新混合系统的生产必将使加拿大制造业受益，以匹配到 2022 年预测的全球 3D 打印复合年增长率 28.5%。将为至少十名总部人员提供尖端技术方面的培训。