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

智能机器人加工系统：集成工艺规划、监控和控制

• 批准号: RGPIN-2019-05873
• 负责人: Khoshdarregi, Matt
• 金额: $ 1.97万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681877
• 关键词: Intelligent; Robotic; Machining; Systems; Integrated

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.

制造业是加拿大现代经济的基石，占加拿大商品出口的68%(3540亿美元)，占GDP总额的10%(1740亿美元)以上[GC，2018]。机械加工是制造航空航天、汽车、电子、生物医学和能源行业精密金属零件的最广泛使用的操作。目前，机械加工工艺的优化需要通过设计、工艺规划、监控和检查步骤进行多次迭代和直接的人工监督。这些迭代增加了最终产品的成本和交货期，并占用了工程资源。与被称为工业4.0(智能工厂)的新工业范式保持一致，我的研究计划的长期目标是开发下一代“智能加工系统”，该系统可以自主地为给定零件生成最佳加工策略，在操作过程中监控自己的加工过程，相互之间进行实时内部交流，并做出在线纠正决策。我目前的重点是智能机器人加工(RM)系统。由于其灵活性、可重构性和相对较大的工作空间，工业机器人在机械加工中的应用引起了研究人员和制造商的极大兴趣，特别是在过去的5-10年里。然而，众所周知，机器人的结构刚度低，精度低，这阻碍了它们在航空航天和汽车工业的精密加工应用中的广泛使用。我目前的研究旨在通过集成基于物理的金属切割模型、监控系统和主动控制策略来解决这些问题。本应用程序追求以下两个短期目标：*1)基于模型的工艺规划和机器人加工操作的监控：*将对加工机器人的结构动力学进行建模。机械加工过程中臂的变形将基于估计的切削力进行预测。工件的放置、加工方向和机器人的姿势都将进行优化，以实现最小的偏差。将调整刀具路径以补偿预测的偏差误差。*2)机器人加工系统中的在线工艺修正：*将通过监控伺服关节的控制需求来间接测量切削力。通过分析所测振动的频率成分，可以检测到颤振不稳定性。进给速度和主轴速度将在面对过度偏转和振动时进行智能在线调整。*将使用内部开发的开放式机器人加工系统来实施这些概念。受训人员(1名博士、3名硕士、1名SS)将获得先进机械加工、工业机器人以及过程监测和控制方面的基本知识和独特技能，这些技能在加拿大航空航天和汽车行业尤为抢手。