Spindle thermal error prediction approach based on thermal infrared images: A deep learning method

Wu, Chengyang; Xiang, Sitong; Wansheng, Xiang

doi:10.1016/j.jmsy.2021.01.013

Cited by 60 publications

(15 citation statements)

References 29 publications

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“…It is demonstrated in section 5 that ANNs showed higher accuracy (in μm and %) than linear models. Thanks to thermal error compensation based on a machine learning model the machining error was reduced by at least 30% [10,11,14,15,17,23,26,32].…”

Section: Gist Of Machine Learningmentioning

confidence: 99%

“…It appears that most often a few or 10-20 sensors have been used. In the most accurate thermal error modelling methods the following numbers of sensors were used: 12 temperature sensors [20], 8 temperature sensors [23] and 6 temperature sensors and an infrared camera [10]. In order to determine a sufficient number of sensors, we divided the modelling methods into three groups using: a few, 10-20 and more than 20 temperature sensors.…”

Section: Measurement Of Input Quantitiesmentioning

confidence: 99%

“…The thermal error has also been modelled as a function of: an infrared camera image [6,10], temperature and spindle rotational speed measurements [13,23] or measurements of temperatures and deformations of specific machine parts [7]. The rotational speed of the spindle has a direct effect on the heating of specific machine tool structure components.…”

Section: Measurement Of Input Quantitiesmentioning

confidence: 99%

“…In the thermal error modelling methods showing the highest accuracy the following data preprocessing methods have been used: partial correlation analysis [20], scaling [23] and infrared camera image filtration [10]. In order to evaluate the usefulness of data preprocessing we compared two groups of thermal error modelling methods: methods using preprocessing and methods not using preprocessing.…”

Section: Data Processingmentioning

confidence: 99%

“…Resampling methods are used to balance the data set, while scaling increases modelling accuracy. Also filtering is a noteworthy method of measurements processing as it has been used to remove noise from measurement series [43] and infrared camera images [10]. Moreover, the dynamics of thermal changes can have a significant bearing on the thermal error and so it should be taken into account by calculating the rate of temperature change over time.…”

Section: Data Processingmentioning

confidence: 99%

See 4 more Smart Citations

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Czwartosz¹,

Jędrzejewski²

2022

Journal of Machine Engineering

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The current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.

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Section: Gist Of Machine Learningmentioning

confidence: 99%

Section: Measurement Of Input Quantitiesmentioning

confidence: 99%

Section: Measurement Of Input Quantitiesmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

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Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Czwartosz¹,

Jędrzejewski²

2022

Journal of Machine Engineering

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Thermal error modeling of motorized spindle based on Elman neural network optimized by sparrow search algorithm

Zhu

Dai

et al. 2022

Int J Adv Manuf Technol

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Thermal error modeling of electric spindle based on particle swarm optimization-SVM neural network

Zhu²,

Zhu³

et al. 2022

Int J Adv Manuf Technol

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High-speed electric spindle is an important part of CNC machining equipment. The thermal displacement generated by the electric spindle during operation is the main reason that affects the machining stability and machining accuracy of the electric spindle. Compensating the thermal error of the high-speed electric spindle can effectively improve the CNC machining. The processing performance of the equipment. Therefore, it is particularly important to establish the accuracy of the thermal error prediction model. Taking the A02 high-speed electric spindle as the research object, ANSYS is used to analyze the thermal characteristics of the electric spindle, and the temperature and thermal displacement monitoring points of the electric spindle are arranged according to the simulation results, and the temperature and thermal displacement data of the monitoring points under different rotational speeds are collected; Using K-means to classify temperature measurement points, uses the grey relation analysis degree to determine the correlation between the temperature measurement point and the thermal displacement data, and selects 4 temperature sensitive points from 10 temperature measurement points. Finally, particle swarm optimization (PSO) is used to optimize the penalty factor and kernel function of support vector machine (SVM), and the PSO-SVM prediction model is established to compare with the neural network prediction model of SVM and genetic algorithm (GA) optimized SVM respectively. The results show that PSO-SVM has better robustness, stability and generalization ability.

show abstract

Spindle thermal error prediction approach based on thermal infrared images: A deep learning method

Cited by 60 publications

References 29 publications

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Thermal error modeling of motorized spindle based on Elman neural network optimized by sparrow search algorithm

Thermal error modeling of electric spindle based on particle swarm optimization-SVM neural network

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