Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

Liu, Hui; Miao, Enming; Wang, Jingfan; Zhang, Liyin; Zhao, Siyu

doi:10.3390/machines10060427

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…

where

Is the weight of the regular term. According to previous research [ 18 ], the value of

should be small and does not need to be accurate, 0.1 is taken in this study. The role of

is explained in detail in the discussion session.…”

Section: Principle Of Trajectory-position Measurement Methods (Tpmm)mentioning

confidence: 99%

Impact Velocity Measurement Method Based on Trajectory and Impact Position

Liu

Wang

2022

Sensors

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The impact velocity of falling weight is an instantaneous quantity. Currently, measurement of impact velocity relies on high-speed sensors to capture the moment of impact. The trajectory-position measurement method (TPMM) is proposed in this study. The main steps are: (1) The impact position is used to capture the impact time. It can be measured when the falling weight is stationary. (2) The discrete falling trajectory is measured and a new empirical regression algorithm is proposed to fit the expression of falling trajectory. (3) The impact velocity is obtained by taking the impact time into the first derivative of the trajectory expression. For 1–5 m falling height, the simulation shows that the relative maximum error and relative expanded uncertainty of the proposed method are less than 0.481% and 0.442%, respectively. Then, the actual experiment is carried out to verify the simulation. The proposed method has high accuracy and low uncertainty. The reasons are: (1) Only a low-speed displacement sensor is need for impact velocity measurement. It is easier to improve accuracy and stability of a low-speed sensor. (2) The empirical regression algorithm can improve the stability of falling trajectory fitting.

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“…

where

Is the weight of the regular term. According to previous research [ 18 ], the value of

should be small and does not need to be accurate, 0.1 is taken in this study. The role of

is explained in detail in the discussion session.…”

Section: Principle Of Trajectory-position Measurement Methods (Tpmm)mentioning

confidence: 99%

Impact Velocity Measurement Method Based on Trajectory and Impact Position

Liu

Wang

2022

Sensors

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show abstract

“…In general, a higher number of placed measurement points is advantageous for accurately calculating thermal deformations. However, due to issues such as noise and correlations among temperature signals [12] , an excess of temperature measurement points may adversely affect fitting accuracy. Through a thermal analysis of the lathe's structure and its primary heat-generating components and by utilizing a thermal imaging camera to observe temperature variations across different parts of the machine tool during the spindle heating process, the final arrangement of temperature measurement points is determined based on the results of thermal characteristic analysis and thermal imaging, as illustrated in Figure 3.…”

Section: Spindle Temperature Measurement Point Layout Strategy For La...mentioning

confidence: 99%

Research on the temperature measurement point selection technology for multi-degree-of-freedom thermal error compensation of lathe spindle

Chen,

Yin,

Huang

et al. 2024

J. Phys.: Conf. Ser.

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In order to solve the problem of difficulty in selecting temperature measurement points caused by the inconsistent axial and radial thermal deformation trends of CNC lathes, a multi-degree-of-freedom joint point selection method is proposed. The Ant Colony Optimization (ACO) algorithm is employed to identify a combination of temperature measurement points. Based on this combination, thermal errors in both directions are modeled, with modeling accuracy serving as the condition for updating ant pheromones. The process iterates until the optimal temperature measurement point combination is determined. Experimental results demonstrate that the temperature measurement points selected using this method exhibit significantly improved fitting accuracy and stability for various operating conditions compared to traditional point selection methods.

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“…This method signi cantly mitigated multicollinearity issues among temperature data. Liu et al [19] presented a new TPS approach to ensure the choice of more stable and less variable temperature points. With the advancement of arti cial intelligence in the manufacturing domain, the application of deep learning methods in the eld of thermal error has become increasingly profound [20].…”

Section: Introductionmentioning

confidence: 99%

Intermittent multivariate time series spindle thermal error prediction under wide environmental temperature ranges and diverse scenario conditions

Jia,

Zhang,

Shen

et al. 2024

Int J Adv Manuf Technol

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As the integration of mechanical engineering and deep learning elds becomes increasingly intertwined, the application of experimental thermal error modeling in intelligent manufacturing has gained signi cant importance. In this paper, the issue of spindle thermal error is treated as a multivariate time series problem due to the thermal transfer characteristics. This study aims to address the challenge of modeling intermittent multivariate time series spindle thermal errors under a wide range of environmental temperatures and various operational scenarios. To tackle this challenge, a substantial volume of experimental data, capable of effectively re ecting the patterns of spindle thermal error variations, was collected through experiments conducted at multiple speeds and under various operational scenarios.Subsequently, the acquired thermal error data underwent intermittent multivariate time series transformation (IMTS) to suit the serialized deep learning model. The study introduces the Crossformer model into the eld of thermal error modeling for the rst time, which is a variant of the Transformer model. The Crossformer model exhibits remarkable adaptability to temporal aspects while effectively maintaining its focus on data features. Ultimately, this study resulted in the development of the IMTS-CrossformerR experimental thermal error model. Throughout the research, a comprehensive examination of various models was undertaken, including two traditional Transformer models, and other thermal error deep learning and machine learning models. The results indicate that the proposed model outperforms its counterparts across multiple model metrics and predictive capabilities. Particularly noteworthy is its substantial improvement in the Range (± 5) ratio of residual uctuations reaching 95.7%, a key engineering metric. These ndings emphasize the signi cant engineering application value of this research, offering novel methods and insights for the precise prediction of spindle thermal errors in the manufacturing industry.

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Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

Cited by 8 publications

References 34 publications

Impact Velocity Measurement Method Based on Trajectory and Impact Position

Impact Velocity Measurement Method Based on Trajectory and Impact Position

Research on the temperature measurement point selection technology for multi-degree-of-freedom thermal error compensation of lathe spindle

Intermittent multivariate time series spindle thermal error prediction under wide environmental temperature ranges and diverse scenario conditions

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