Optimal measurement area of articulated coordinate measuring machine calculated by ant colony algorithm

Yi, 胡毅 Hu; Chao, 江 超 Jiang; Wei, 黄 炜 Huang; Peng-hao, 胡鹏浩 Hu

doi:10.3788/ope.20172506.1486

Cited by 3 publications

(1 citation statement)

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“…The optimal measuring area is related to the error characteristics of the angle measuring system. An ant colony algorithm was used to determine the optimal measurement area for an AACMM [27].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Huang

et al. 2019

Applied Sciences

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Articulated arm coordinate measuring machines (AACMMs) have been developed and applied in industrial measurement fields for more than 30 years. Manual operation is typically required during measurement, which introduces uncertain influences, such as fluctuation of measurement force, speed, and acceleration, and leads to poor reliability and reproducibility. In this paper, a novel self-driven joint model is proposed to realize automatic measurement for AACMMs. A self-driven joint is designed by combining the joint of an AACMM with a robotic arm to realize automatic rotation. A self-driven AACMM is designed using three rolling joints and three pitching joints with assigned parameters. A virtual prototype of the self-driven AACMM is established using the Adams software to simulate the driving moment of each joint. The simulation results demonstrate that the designed mechanical structure and selected devices can meet the preset requirements. Additionally, based on the proposed model, a single physical joint is developed and assembled for performance testing. Experimental results demonstrate that the model can achieve a repeatability of 1.39″ (k = 2) when the rotational velocity is less than 1.53 rad/s. Therefore, the proposed design is suitable for use in AACMMs.

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Section: Introductionmentioning

confidence: 99%

Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Huang

et al. 2019

Applied Sciences

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Research on the method of determining the best measuring area for the circular surface survey

Yang

et al. 2021

Int J Adv Manuf Technol

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Particle swarm algorithm-based identification method of optimal measurement area of coordinate measuring machine

Chen,

Wu,

Gao

et al. 2024

Review of Scientific Instruments

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A particle swarm algorithm-based identification method for the optimal measurement area of large coordinate measuring machines (CMMs) is proposed in this study to realize the intelligent identification of measurement objects and optimize the measurement position and measurement space using laser tracer multi-station technology. The volumetric error distribution of the planned measurement points in the CMM measurement space is obtained using laser tracer multi-station measurement technology. The volumetric error of the specified step distance measurement points is obtained using the inverse distance weighting (IDW) interpolation algorithm. The quasi-rigid body model of the CMM is solved using the LASSO algorithm to obtain the geometric error of the measurement points in a specified step. A model of individual geometric errors is fitted with least squares. An error optimization model for the measurement points in the CMM space is established. The particle swarm optimization algorithm is employed to optimize the model, and the optimal measurement area of the CMM airspace is determined. The experimental results indicate that, when the measurement space is optimized based on the volume of the object being measured, with dimensions of (35 × 35 × 35) mm3, the optimal measurement area for the CMM, as identified by the particle swarm algorithm, lies within the range of 150 mm < X < 500 mm, 350 mm < Y < 700 mm, and −430 mm < Z < −220 mm. In particular, the optimal measurement area is defined as 280 mm < X < 315 mm, 540 mm < Y < 575 mm, and −400 mm < Z < −365 mm. Comparative experiments utilizing a high-precision standard sphere with a diameter of 19.0049 mm and a sphericity of 50 nm demonstrate that the identified optimal measurement area is consistent with the results obtained through the particle swarm algorithm, thereby validating the correctness of the method proposed in this study.

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Optimal measurement area of articulated coordinate measuring machine calculated by ant colony algorithm

Cited by 3 publications

References 0 publications

Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Design and Validation of a Self-Driven Joint Model for Articulated Arm Coordinate Measuring Machines

Research on the method of determining the best measuring area for the circular surface survey

Particle swarm algorithm-based identification method of optimal measurement area of coordinate measuring machine

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