Pose-dependent Cutting Force Identification for Robotic Milling

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-03-17 DOI:10.1115/1.4062145
Maxiao Hou, Hongru Cao, Yang Luo, Yanjie Guo
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引用次数: 1

Abstract

Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.
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基于位姿的铣削机器人切削力辨识
切削力识别是提高工业机器人性能和降低加工振动的关键。然而,由于机器人铣削系统的动态特性随机器人姿态的变化而变化,大多数切削力的间接识别方法都不适用。本文提出了一种利用机器人铣削产生的加速度信号识别切削力的新方法。首先,将机器人在不同加工点的模态参数作为训练数据集,建立高斯过程回归模型;然后,利用GPR模型预测的模态参数,基于最小方差无偏估计方法对切削力估计进行优化。然后,利用卡尔曼滤波方法对切削力辨识误差和状态估计误差的协方差矩阵进行更新;最后,通过实验验证了该方法的有效性,结果表明,在机器人姿态可变的情况下,该方法的识别误差和时间是可以接受的。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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