Autonomous Identification of Machining Dynamics for Intelligent Vibration Suppression in Milling Operations

自主识别加工动力学，实现铣削操作中的智能振动抑制

• 批准号: 575261-2022
• 负责人: Ahmadi, KeivanK
• 金额: $ 1.82万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=759892
• 关键词: Autonomous; Identification; Machining; Dynamics; Intelligent

Machining is the most used process in today's manufacturing. The global machining industry was estimated to be worth about $350 billion in 2019 with the Compound Annual Growth Rate estimated at about 7%. Chatter during machining processes is a strategic problem that undermines process productivity. Chatter refers to unstable vibrations during machining, which must be avoided at all costs otherwise it damages the machine tool and the machined surface. Current chatter avoidance methods either require costly trials or long machine downtimes and expensive vibrations tests. Besides, the current methods are incompatible with intelligent and autonomous manufacturing systems that are increasingly being adopted to increase productivity. Combining the theoretical and technological competencies of the University of Victoria and the Technical University of Munich, this project will develop a new approach to chatter avoidance that is autonomous, does not require machine downtime, and can be incorporated into the modern cyber-physical machine tools. The benefits of the proposed research to the Canadian manufacturing industry are in reducing their production cost and environmental footprint. The envisioned autonomous chatter avoidance system will reduce manufacturing cycle-time, machine downtime, and material waste due to accelerated tool wear and breakage or scrapped parts. These factors are major contributors to the cost and environmental footprint of machining processes, which will be reduced by the proposed project. The international collaboration enabled by this research will contribute to the digitization of the Canadian manufacturing industry, which employs more than 1.7 million Canadians and pays more in salaries than any other sector in Canada.

机械加工是当今制造业中最常用的工艺。预计2019年全球机械加工行业价值约为3500亿美元，复合年增长率约为7%。加工过程中的颤振是一个影响过程生产率的战略问题。颤振是指加工过程中不稳定的振动，必须不惜一切代价避免这种振动，否则会损坏机床和加工表面。目前的避免颤振的方法要么需要昂贵的试验，要么需要较长的机器停机时间和昂贵的振动测试。此外，当前的方法与越来越多地被用来提高生产率的智能和自主制造系统不兼容。该项目结合维多利亚大学和慕尼黑工业大学的理论和技术能力，将开发一种新的避免颤振的方法，该方法是自主的，不需要机器停机，并且可以合并到现代网络物理机器工具中。拟议研究对加拿大制造业的好处在于降低生产成本和环境足迹。设想的自主颤振避免系统将减少制造周期时间、机器停机时间以及由于加速工具磨损和破损或报废零件而造成的材料浪费。这些因素是机械加工过程成本和环境足迹的主要因素，拟议项目将减少这些成本和环境足迹。这项研究促成的国际合作将有助于加拿大制造业的数字化，该行业雇用了超过 170 万加拿大人，支付的工资比加拿大任何其他行业都高。