Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm

Yue, Haitao; Guo, Chenguang; Li, Qiang; Zhao, Lijuan; Hao, Guangbo

doi:10.1007/s40430-020-02514-z

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…At different rotational speeds, the motorized spindle has axial and radial thermal displacements. Compared with the axial thermal displacement, the radial thermal drift is minimal and can be ignored [25]. In this paper, the axial thermal error data of the motorized spindle are collected by the eddy current displacement sensor.…”

Section: Experimental Platform Constructionmentioning

confidence: 99%

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Li¹,

Zhu²,

Dai

et al. 2022

Preprint

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The thermal error of the motorized spindle is an essential factor affecting the machining accuracy of high-speed CNC machines. The establishment of a high-speed motorized spindle thermal error model for thermal error compensation can effectively improve the impact of thermal errors on the machining accuracy of the machine tool. This paper proposes a sparrow search algorithm to optimize the Elman neural network to predict thermal errors in motorized spindles. First, thermal characteristics of the A02 high-speed motorized spindle were analyzed using ANSYS Workbench software. Based on the simulation results, the position of the temperature measuring points is arranged in the temperature and thermal error experiment of the motorized spindle. The temperature and thermal displacement data of high-speed motorized spindle at different rotational speeds were collected; Secondly, the method of combining pedigree clustering and k-means clustering is used to perform cluster analysis on each temperature measurement point, and the grey correlation degree is used to determine the correlation between temperature measurement points and thermal error. Three temperature-sensitive points were screened from ten temperature measurement points, which reduced the collinearity between temperature measurement points and the number of independent variables of the model. Finally, the weights and thresholds of the Elman neural network are optimized by the sparrow search algorithm, and the thermal error prediction model of motorized spindle based on SSA-Elman neural network is established and compared with Elman Neural Network and Particle Swarm optimized Elman Neural Network prediction model. The results show that the SSA-Elman neural network model has the highest prediction accuracy and exhibits good stability and generalization ability.

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Section: Experimental Platform Constructionmentioning

confidence: 99%

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Li¹,

Zhu²,

Dai

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The motorized spindle has axial and radial thermal drift at different speeds. Compared with axial thermal drift, radial thermal drift is very small to be ignored [27,28]. In this paper, the LK-H020 laser displacement sensor is used to collect axial thermal error data of the motorized spindle.…”

Section: Experiments On Temperature Field and Thermal Error Of Motorized Spindle 21 Experimental Platform Constructionmentioning

confidence: 99%

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Zhu

Dai

et al. 2021

Machines

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High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

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“…The experimental results of servo system simulation test bench show that the proposed method can effectively optimize the parameters of servo system controller and improve the quality of servo system [3]. Yue et al (2020) believed that advanced CNC machine tools are more and more important than ever before, and the problem of fault diagnosis of CNC machine tools is becoming more and more important. Traditional failure mode, influence analysis and critical analysis were combined with fuzzy theory to analyse the faults of the spindle system of CNC machine tools to avoid unnecessary faults and realize the safe and reliable operation of the equipment.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional failure mode, influence analysis and critical analysis were combined with fuzzy theory to analyse the faults of the spindle system of CNC machine tools to avoid unnecessary faults and realize the safe and reliable operation of the equipment. In addition, the critical failure modes of key components and spindle system on CNC machine tools that need special attention were determined [4].…”

Section: Introductionmentioning

confidence: 99%

Control Parameter Optimization-Based Machining Control Technology of State-of-the Art Computer Numerical Control Machine Tools

2021

IJOMAM

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In order to improve the accuracy, reliability and efficiency of computer numerical control (CNC) machine tools, first, according to the working principle of servo motor and related physical relations, the mathematical model of servo system is established. Moreover, the mathematical model is used to establish the three-closed loop simulation structure of servo. Then, according to the simulation structure, a parameter optimization algorithm of CNC machine tool servo system based on improved particle swarm optimization (PSO) algorithm is proposed. Compared with the previous PSO algorithm, the PSO algorithm used has two improvements. First, it introduces the nonlinear dynamic inertia weight coefficient, and constructs the functional relationship between accelerated factor and inertia weight. Second, it introduces the extreme perturbation operator to increase the search range of particles. Moreover, the improved PSO algorithm is used to optimize the scale factor parameters in the regulator. The experimental results show that the dynamic performance of CNC machine tool servo system has been improved to a certain extent. Compared with the Proportion Integration Differentiation (PID) parameters without tuning, the overshoot of the system is reduced and the robustness is improved. In addition, the tuning PID parameters are compared with those of crowd search algorithm (SOA), PSO and genetic algorithm (GA). The results show that the adjustment time to the stable error of the CNC machine tool servo system optimized by GA algorithm is longer than other algorithms, and the oscillation of the transition time is severer. The PID parameters of CNC machine tool servo system optimized by improved PSO algorithm are obviously better than those optimized by other algorithms. The system using this parameter has a short adjustment time and an overshoot of 0. Therefore, the performance of the improved PSO algorithm is better than SOA, PSO and GA algorithm. The proposed algorithm provides a new idea for intelligent algorithm to solve the control parameter optimization problem of CNC machine tools.

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Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm

Cited by 17 publications

References 28 publications

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Control Parameter Optimization-Based Machining Control Technology of State-of-the Art Computer Numerical Control Machine Tools

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