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Related Experiment Video

Updated: Jun 8, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A hyperparameter optimization-assisted deep learning method towards thermal error modeling of spindles.

Shicun Ao¹, Sitong Xiang¹, Jianguo Yang²

¹Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China; Ningbo Key Laboratory of Micro-nano Motion and Intelligent Control, China.

ISA Transactions

|November 8, 2024

Summary

This summary is machine-generated.

This study introduces a novel neural network model for precise spindle thermal error prediction in machine tools. The model integrates Bayesian optimization with dilated convolution neural networks, achieving over 95% accuracy.

Keywords:

Bayesian optimization Convolutional neural network Dilated convolution Spindles Thermal error modeling

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Area of Science:

Mechanical Engineering
Artificial Intelligence
Machine Learning

Background:

Spindle thermal errors are critical factors affecting machine tool accuracy.
Deep learning models for thermal error compensation require careful network design and hyperparameter tuning for optimal performance.

Purpose of the Study:

To develop a robust neural network model for accurate prediction of spindle thermal errors.
To enhance the generalization ability and performance of deep learning models in machine tool accuracy applications.

Main Methods:

Integration of Bayesian Optimization (BO) with Dilated Convolutional Neural Networks (DCNN).
Utilization of Gaussian Processes (GP) for hyperparameter tuning to avoid local optima.
Optimization of 9 critical hyperparameters within the DCNN architecture.

Main Results:

The proposed BO-DCNN model demonstrated high accuracy in predicting radial thermal errors.
Achieved over 95% prediction accuracy for both heating and cooling states in X and Y directions.
Dilated convolutions expanded the receptive field without increasing computational cost.

Conclusions:

The developed Bayesian optimization-integrated DCNN model offers a precise and effective solution for spindle thermal error modeling.
This approach improves the accuracy and reliability of machine tools by addressing thermal error challenges.
The method shows significant potential for real-world applications in precision manufacturing.