Parameterized finite element modeling and experimental modal testing for vibration analysis of an industrial hexapod for machining

Pham, Minh Hong; Champliaud, Henri; Liu, Zhaoheng; Bonev, Ilian A.

doi:10.1016/j.mechmachtheory.2021.104502

“…The machining-based ballbar test utilized two custom fixtures (A and B in Fig. 4, d) attached to the back of the electrical spindle and frame bar of the HMC (more details are available in [26,34]). During the measurement, the floor-mounted hexapod moved in the YZ plane while the wall-mounted hexapod moved along the X-axis to allow the workpiece to make contact with the cutting tool for a fixed-depth circular cut.…”

Section: Ballbar Setup For Machining Operationsmentioning

confidence: 99%

Influence of Machining Parameters on Dynamic Errors in a Hexapod Machining Cell

Xing,

Bonev,

Liu

et al. 2023

Preprint

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Dynamic errors from the robotic machining process can negatively impact the accuracy of manufactured parts. Currently, effectively reducing dynamic errors in robotic machining remains a challenge due to the incomplete understanding of the relations hip between machining parameters and dynamic errors, especially for hexapod machining cell. To address this topic, a dynamic error measurement strategy combining a telescoping ballbar, an Unscented Kalman Filter (UKF), and particle swarm optimization (PSO) was utilized in robotic machining. The machining parameters, including spindle speed, cutting depth, and feeding speed, were defined using the Taguchi method. Simultaneously, vibrations during machining were also systematically measured to fully comprehend the nature of dynamic errors. Experimental results indicate that dynamic errors in a hexapod machining cell (HMC) are significantly amplified in machining setups, ranging from 4 to 20 times greater compared to non-machining setups. These errors are particularly influenced by machining parameters, especially for spindle speed. Furthermore, the extracted dynamic errors exhibit comparable frequency distributions, such as spindle frequency and tool passing frequency, to the vibration signals obtained at the chosen sampling rate. This expands the application and enhances the comprehension of dynamic errors for spindle and cutting tool condition recognition.

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“…The machining-based ballbar test utilized two custom fixtures (A and B in Fig. 4, d) attached to the back of the electrical spindle and frame bar of the HMC (more details are available in [26,34]). During the measurement, the floor-mounted hexapod moved in the YZ plane while the wall-mounted hexapod moved along the X-axis to allow the workpiece to make contact with the cutting tool for a fixed-depth circular cut.…”

Section: Ballbar Setup For Machining Operationsmentioning

confidence: 99%

“…However, their limited accuracy resulting from the low stiffness of the serial chain restricts their widespread application in robotic machining [14]. To tackle this challenge, a parallel kinematic robots-based machining cell has been developed, consisting of two Fanuc F-200iB hexapod industrial robots [26]. An algorithm was developed to efficiently calculate the inverse kinematics of the hexapod, enabling the planning of optimal trajectories.…”

Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on dynamic errors in a hexapod machining cell

Xing,

Bonev,

Liu

et al. 2024

Int J Adv Manuf Technol

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Dynamic errors from the robotic machining process can negatively impact the accuracy of manufactured parts. Currently, effectively reducing dynamic errors in robotic machining remains a challenge due to the incomplete understanding of the relations hip between machining parameters and dynamic errors, especially for hexapod machining cell. To address this topic, a dynamic error measurement strategy combining a telescoping ballbar, an Unscented Kalman Filter (UKF), and particle swarm optimization (PSO) was utilized in robotic machining. The machining parameters, including spindle speed, cutting depth, and feeding speed, were defined using the Taguchi method. Simultaneously, vibrations during machining were also systematically measured to fully comprehend the nature of dynamic errors. Experimental results indicate that dynamic errors in a hexapod machining cell (HMC) are significantly amplified in machining setups, ranging from 4 to 20 times greater compared to non-machining setups. These errors are particularly influenced by machining parameters, especially for spindle speed. Furthermore, the extracted dynamic errors exhibit comparable frequency distributions, such as spindle frequency and tool passing frequency, to the vibration signals obtained at the chosen sampling rate. This expands the application and enhances the comprehension of dynamic errors for spindle and cutting tool condition recognition.

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“…[11][12][13]). So, the influence of these deterministic and stochastic factors on the screening process is not a deterministic linear relationship, but a very complex non-linear relationship [14][15]. In the existing research, the dynamic model of the system has not been established for probability screening system, and the relationship between AR model and dynamic model has not been established to obtain the dynamic characteristic parameters, the factors that affect the screening efficiency of the system itself have not been identified.…”

Section: Introductionmentioning

confidence: 99%

Prediction of comprehensive dynamic performance for probability screen based on AR model-box dimension

Chen,

Huang

2023

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In order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value.

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Parameterized finite element modeling and experimental modal testing for vibration analysis of an industrial hexapod for machining

Cited by 17 publications

References 31 publications

Influence of Machining Parameters on Dynamic Errors in a Hexapod Machining Cell

Influence of Machining Parameters on Dynamic Errors in a Hexapod Machining Cell

Influence of machining parameters on dynamic errors in a hexapod machining cell

Prediction of comprehensive dynamic performance for probability screen based on AR model-box dimension

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