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

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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.