Application of modified artificial bee colony algorithm to flatness error evaluation

Jun, 罗钧 Luo; Qiang, 王强 Wang; Li, 付丽 Fu

doi:10.3788/ope.20122002.0422

Cited by 12 publications

(4 citation statements)

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“…The results show that the algorithm is better than other algorithms and easy to implement. Luo et al (2012) proposed to apply the improved artificial bee colony algorithm to the evaluation of the minimum zone of flatness error. Experiments show that it is suitable for form and position error measuring instruments and CMM.…”

Section: Related Workmentioning

confidence: 99%

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Han

Zou

Chen

et al. 2021

Front. Bioeng. Biotechnol.

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Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

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Section: Related Workmentioning

confidence: 99%

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Han

Zou

Chen

et al. 2021

Front. Bioeng. Biotechnol.

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show abstract

“…Regarding the research work on flatness error measurement of large-size workpiece planes, relevant research mainly focuses on the direction of flatness error assessment methods [9][10]. Moreover, the data preprocessing methods for measuring large-sized planes are different, and there is relatively little research work in related areas [11][12].…”

Section: Introductionmentioning

confidence: 99%

Data preprocessing for flatness measurements of large precision workpieces

Huang,

Pang,

Xia

2024

J. Phys.: Conf. Ser.

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Measuring the flatness error of large precision workpieces quickly and accurately is a difficult problem. A new method for preprocessing flatness measurement data based on MSE (mean squared error) is proposed. A mathematical model of a new data preprocessing method was established, and the mathematical formula for model solving was derived in detail. The data were measured by digital level on the plane of the granite base with dimensions of 2340 m×1540 mm. The new method and SmartLevel (basic measurement system of the level computer) were used to calculate and process the data. The flatness errors after diagonal evaluation were 4.07 μm and 3.90 μm, respectively. The relative error of the two was 4.36%, which confirmed the reliability and accuracy of the new method. The data results show that this method can be effectively used for the engineering measurement of the flatness of large precision workpieces.

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“…For example, we can use a three-coordinate measurement machine or self-collimator to measure it directly. Similarly, there are many algorithms such as the minimum zone method [18], the rotation method based on genetic algorithm [19], the geometry searching approximation algorithm [20] and the modified artificial bee colony algorithm [21], which calculate the flatness indirectly. In addition, after expanding the EST method from line to plane, it is also widely used in measuring flatness [3,22,23], especially for the large-scale plane and onmachine conditions.…”

Section: Introductionmentioning

confidence: 99%

Novel on-machine measurement system and method for flatness of large annular plane

Wang

Sun

Yuan

2019

Meas. Sci. Technol.

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Flatness plays an important role in the assembly process of aircraft engine rotors, especially the flange plane that is annular between the rotor shaft and cone. It determines the contact stiffness and assembly accuracy of the engine rotor. However, a specific method to measure the annular plane is lacking, especially in the on-machine conditions and for on-machine measurement. As a low-cost, high-precision and easy-to-use measurement method, error separating techniques (ESTs) are widely used for flatness measurement. However, when applying them, the initial error and probe fixture tilt error cannot be eliminated at the same time. This paper proposes a novel design of an on-machine measurement system and method for annular plane flatness, which can be applied to the assembly of aeroengine rotors. The novel method proposed in this paper combines the advantages of ESTs and utilizes the properties of the annular plane to eliminate the two main errors successfully. At the same time as processing data, the equivalent homogenization processing and 3D least squares method are introduced to further improve the credibility of the data. According to the method above, in order to meet the needs of on-machine measurement, a novel device that can adjust two axes and that is equipped with an indexing plate is designed. The measuring system uses the Keyence GT2-H12K contact probe, which can level itself by using the probe values to control two axes. Finally, the annular plane was measured by a three-coordinate measuring machine (CMM). The result of the CMM is 47.6 µm, which is close to the 45.9 µm measured by the novel on-machine conditions method, which proves the reliability of the method proposed in this paper.

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Application of modified artificial bee colony algorithm to flatness error evaluation

Cited by 12 publications

References 0 publications

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Data preprocessing for flatness measurements of large precision workpieces

Novel on-machine measurement system and method for flatness of large annular plane

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