Rapid and robust on-site evaluation of articulated arm coordinate measuring machine performance

Asmai, Salma El; Hennebelle, François; Coorevits, Thierry; Vincent, Renald; Fontaine, Jean-François

doi:10.1088/1361-6501/aade10

Cited by 7 publications

(5 citation statements)

References 39 publications

(51 reference statements)

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“…Many methods have been proposed to evaluate the performance of the AACMM calibration, including ASME B89.4.22-2004 standard [25], ISO 10360-12 standard [26], VDI/VDE 2617 9:2009 standard [27] and a rapid verification method proposed by EI Asmai et al [28,29]. In this work, two verification tests for the AACMM calibration are chosen, including the single-point test based on the ASME B89.4.22-2004 and length test based on the ISO 10360-12.…”

Section: Verification Experimentsmentioning

confidence: 99%

On-site calibration method of the AACMM based on a high-precision CNC machine tool

Wang

Sun²,

Sun³

et al. 2022

Meas. Sci. Technol.

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An articulated arm coordinate measuring machine (AACMM) is a kind of instrument used to test workpieces in the industrial field, and its measurement uncertainty depends on the on-site environmental factors to a certain extent. If there is a problem with the AACMM during on-site testing, it needs to be re-calibrated to ensure the measurement uncertainty. In this work, a fast on-site calibration method of the AACMM is proposed using a high-precision CNC machine tool. The AACMM is fixed on the machine tool table. A calibration artifact is clamped on the machine tool spindle. This calibration artifact could move to any point in the global space of the AACMM along with the operation of the spindle. Then, a virtual calibration artifact with calibration points is achieved using the CNC machine tool. The positions of these points are calculated by the improved Hammersley sequence. The calibration method using a high-precision CNC machine tool is experimentally verified through the single-point articulation performance test of ASME B89.4.22-2004 and the length measurement test of ISO 10360-12. The calibration method proposed in this work only uses an on-site high-precision CNC machine tool, one of the common machines in many industrial fields, as the calibration instrument for the AACMM calibration. No extra equipment is required, which is convenient and beneficial to calibrate the AACMM. And the CNC machine tool could construct different kinds of virtual structures for the AACMM calibration, such as virtual rods, virtual circles, virtual balls, and even non-uniform three-dimensional artifacts with complex curved surfaces. This may arouse many novel calibration methods and further improve the calibration accuracy.

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Section: Verification Experimentsmentioning

confidence: 99%

On-site calibration method of the AACMM based on a high-precision CNC machine tool

Wang

Sun²,

Sun³

et al. 2022

Meas. Sci. Technol.

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show abstract

“…The kinematic parameters of the RAACMM are presented in Table 1, which are from the identification procedure of calibration. The kinematic parameters will be used to calculate the coordinates of the probe with equation (1). In this section, the training data will be acquired firstly.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…Robotic articulated arm coordinate measuring machines (RAACMMs), as specific robots, are widely applied in industrial fields, for example, in automobile manufacturing and product quality control, owing to the advantages of fast operation, high efficiency, and high flexibility. 1,2 However, the accuracy of RAACMMs is relatively lower than traditional coordinate measuring machines, which limits the application of RAACMMs in high-precision industrial fields. The main factors affecting the accuracy of RAACMMs can be divided into static factors and dynamic factors.…”

Section: Introductionmentioning

confidence: 99%

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Gao

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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The robotic articulated arm coordinate measuring machine (RAACMM) is a special robotic structural measuring instrument used to perform industrial field inspection. The accuracy of a RAACMM in different poses presents certain characteristics due to the influence of various dynamic factors. However, the existing error compensation model of the RAACMM cannot include dynamic factors, which imposes certain limits on improving the accuracy of the RAACMM. In this article, a residual correction method for the RAACMM based on a radial basis function neural network (RBFNN) is proposed to compensate the dynamic factors and improve the accuracy of the RAACMM. Firstly, the influence of the pose configuration of the RAACMM on the residual error of the probe is analyzed. The periodic characteristics of the residual error are obtained based on the analysis results. Secondly, a relationship model between the residual errors and the structural parameters is established in the cylindrical coordinate system. Then, a residual correction model based on the single point repeatability and RBFNN is proposed to further enhance the accuracy of the RAACMM. The probe of the RAACMM is constrained with a cone-hole gauge to acquire the single point repeatability data. The residual correction model is trained with the data of single point repeatability, and residual errors are calculated via the residual correction model. Experimental results show that the repeatability and measurement accuracy of the RAACMM are all improved after the residual correction, which validates the effectiveness of the residual correction method.

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“…However, due to factors such as pressure, and wear in industrial areas, the structural parameters of the mechanical arm may change after a period of use. Because the impact of structural parameter errors on measurement results is nonlinear [5][6] , tiny errors in certain structural parameters may lead to observable errors in the measurement results. Consequently, periodically identifying the structural parameters of the mechanical arm is indispensable.…”

Section: Introductionmentioning

confidence: 99%

Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

Xia

Zhong

2023

Preprint

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Accurately identifying the structural parameters of the mechanical arm can effectively increase its precision. Firstly, the kinematic model of the mechanical arm is constructed by adopting the MDH method. Secondly, based on the single-point conical hole repeatability, the objective function characterizing the single-point repeatability error is established. Thirdly, an adaptive chicken swarm optimization algorithm (mCSO) is put forward to tackle the issue of low convergence accuracy of chicken swarm optimization algorithm (CSO). Then, combined with the objective function characterizing the single-point repeatability error, the structural parameters of the mechanical arm are identified using algorithms CSO and mCSO, respectively. Finally, repeat the single-point conical hole repeatability experiment using the mechanical arm before and after identification. The experimental result reveals that the single-point repeatability error of the mechanical arm after mCSO identification is greatly reduced.

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Rapid and robust on-site evaluation of articulated arm coordinate measuring machine performance

Cited by 7 publications

References 39 publications

On-site calibration method of the AACMM based on a high-precision CNC machine tool

On-site calibration method of the AACMM based on a high-precision CNC machine tool

Residual correction for robotic articulated arm coordinate measuring machine with radial basis function neural network

Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

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