Intelligent Robotic Machining Systems: Integrated Process Planning, Monitoring, and Control

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Manufacturing is a cornerstone of Canada's modern economy accounting for 68% ($354 billion) of Canadian merchandise exports and over 10% ($174 billion) of the total GDP [GC, 2018]. Machining is the most widely used operation for manufacturing precision metal parts for the aerospace, automotive, electronics, biomedical, and energy industries. Currently, optimization of machining processes requires numerous iterations and direct human supervision through the design, process planning, monitoring, and inspection steps. These iterations add to the cost and lead times of final products and occupy engineering resources. Aligned with the new industrial paradigm known as Industry 4.0 (Smart Factory), the long-term goal of my research program is to develop next-generation "intelligent machining systems" which can autonomously generate optimal machining strategies for a given part, monitor their own processes during the operation, communicate internally with each other in real time, and make online corrective decisions. My current focus is on intelligent robotic machining (RM) systems. Owing to their flexibility, reconfigurability, and relatively large workspace, utilization of industrial robots for machining applications have gained a significant amount of interest among researchers and manufacturers, particularly over the past 5-10 years. However, robots are known to have low structural stiffness and poor accuracy, which have prevented them from widespread use in precision machining applications in the aerospace and automotive industries. My current research aims at tackling these issues by integrating physics-based metal cutting models, monitoring systems, and active control strategies. The following two short-term objectives are pursued in this application: 1) Model-based process planning and monitoring of robotic machining operations: The structural dynamics of a machining robot will be modeled. The deflections of the arm during a machining operation will be predicted based on the estimated cutting forces. The workpiece placement, machining direction, and the robot's pose will be optimized for minimal deflections. The toolpath will be adjusted to compensate for the predicted deflection errors. 2) Online process correction in robotic machining systems: Cutting forces will be measured indirectly by monitoring the control demand of the servo joints. Chatter instability will be detected by analyzing the frequency contents of the measured vibrations. The feedrate and spindle speed will be intelligently adjusted online in the face of excessive deflections and vibrations. An in-house developed open-architecture robotic machining system will be used for the implementation of the concepts. The trainees (1 PhD, 3 MSc, 1 SS) will gain fundamental knowledge and unique skills in advanced machining, industrial robotics, and process monitoring and control, which are highly sought-after skills particularly in the Canadian aerospace and automotive industries.