Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network

Addressing the limitations of the single-temperature measurement point monitoring for detecting the temperature changes in the CNC machine tool spindle, and the shortcomings of the thermal error model based on back propagation neural network (BP) in accuracy, convergence and robustness. This paper studies the thermal error identification model and method of spindle based on multiple temperature sensors. An Adaptive particle swarm algorithmback-propagation neural network (IAPSO-BP) model for thermal error identification of principal axes is proposed. To enhance modeling accuracy and comprehensively monitor the temperature information of the machine tool spindle, the input of this model is generated by processing the data collected through five temperature sensors. The IAPSO algorithm is employed for the automatic identification of BP parameters, reduce manual intervention, and enhancing the
model's capacity for generalization.

Loại tài liệu:

Technical Report

Tác giả:

Mengjie Zhou

Đề mục:

Vật lý

Nhà xuất bản:

IOP Publishing

Tác giả phụ:

Ling Yin

Ngày xuất bản:

2024

Số trang/ tờ:

Định dạng:

pdf

Định danh tư liệu:

doi:10.1088/1742-6596/2694/1/012069

Nguồn gốc:

Journal of Physics: Conference Series; VOL2694, issue 1, 2024, pages 1-8 (012069)

Liên kết:

ISSN 1742-6596

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2024VOL2694(01)JPC1-8 (012069) Research_on_Thermal_Error_Mode.pdf 633526 Kb Xem Tải

Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network

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