A status review of volumetric positioning accuracy prediction theory and static accuracy design method for multi-axis CNC machine tools

Wu, Huai‐Ning; Li, Xiaoxiao; Sun, Fenglian; Zhao, Yongxin

doi:10.1007/s00170-022-10015-7

Cited by 8 publications

(3 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…𝑥 𝐺𝑀𝑃 = 𝑚𝑎𝑥 (𝑝,𝑞)∈ℛ 𝑖𝑗 𝑥 𝑘𝑝𝑞 (13) where 𝑦 𝑘𝑖𝑗 represents the average pooling output value of the rectangular region 𝑅 𝑖𝑗 related to the 𝑘th feature map [20][21]. 𝑥 𝑘𝑝𝑞 represents the element located at (p, q) in region 𝑅 of the kth feature map.…”

Section: The Structures Of 1d-isacnnmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

Dong,

Zhao,

Cui

2024

Nonlinear Dyn

View full text Add to dashboard Cite

：The complexity of the internal structure of rolling bearings and the harshness of their operating environment result in strong non-stationarity and nonlinear of the vibration signals. It remains a challenging and attractive task to accomplish more accurate classification through signal processing techniques and pattern recognition methods. To realize this aim, a novel one-dimensional improved self-attentionenhanced convolutional neural network (1D-ISACNN) with empirical wavelet transform (EWT) is proposed for rolling bearing fault classification. Firstly, the EWT algorithm is employed to decompose the raw signal into three frequency components, allowing for further extraction of multi-frequency components to enhance signal characteristics. Subsequently, a creative1D-ISACNN, which leverages the merits of new developed attention mechanism and an optimized meta-activation concatenation function in feature learning to better capture and map crucial information within the signal. Furthermore, label smoothing regularization is designed as the loss function of the 1D-ISACNN, which takes into account not only the loss of correctly labeled positions in the training samples but also the loss of other mislabeled positions. Finally, the adaptive moment projection estimation is designed to ensure a more robust gradient update strategy for updating the parameters of the proposed model. The developed model tested on three different sets of bearing data, has achieved a classification accuracy of 100%. The proposed method demonstrates higher recognition accuracy and more robust generalization capabilities compared to other state-of-the-art approaches to ablative experiments.

show abstract

Section: The Structures Of 1d-isacnnmentioning

confidence: 99%

“…The relationship between CE (𝑙 0 ) and LSR (𝑙) is concisely deduced in Eq. (20). By employing smooth labels instead of real labels to alleviate overfitting, it is argued that LSR possesses potential advantages in handling samples.…”

Section: Lsr-based Loss Functionmentioning

confidence: 99%

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

Dong,

Zhao,

Cui

2024

Nonlinear Dyn

View full text Add to dashboard Cite

show abstract

“…Tracking error is closely related to the mechanical structure, control system, operating status, load change, and internal and external disturbance. 2,3 Numerous error compensation and error reduction techniques, 4 related to structural optimization, parameter matching, control methods trajectory optimization, and inertia matching have been developed. According to the object of study, tracking error can be divided into two categories: tracking error of the feed drive system and tracking error of the tool center point (TCP) of the machine tool.…”

Section: Introductionmentioning

confidence: 99%

Influence of lateral dynamic error on the trajectory error in machine tools

Wang

Guo

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

In machine tools, the trajectory error of the tool center point (TCP) is caused due to the dynamic positioning error of feed drive systems according to the relationship in spatial structure and motion. The dynamic positioning error of the feed drive system includes axial dynamic error and lateral dynamic error. To analyze the influence of the lateral dynamic error on the trajectory error of the TCP of multi-axis machine tools, in this study, circular and butterfly trajectories were designed as the set trajectories of a machine tool with three linear axes. In addition, the tracking error models were constructed both considering the lateral dynamic error and without considering the lateral dynamic error, and the difference between the two models was quantified. The Sobol method was then used to analyze the sensitivity of the lateral dynamic error of the feed drive system to the tracking error of the TCP. Finally, experiments were performed on a three-axis machine tool. The results show the maximum lateral dynamic error in the direction perpendicular to the motion direction is 0.026 mm. The lateral dynamic error detrimentally affects the tracking error and contour error of the TCP in multi-axis machine tools.

show abstract

A support vector regression-based method for modeling geometric errors in CNC machine tools

Zhang,

Liu,

Zhou

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

A status review of volumetric positioning accuracy prediction theory and static accuracy design method for multi-axis CNC machine tools

Cited by 8 publications

References 69 publications

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

Influence of lateral dynamic error on the trajectory error in machine tools

A support vector regression-based method for modeling geometric errors in CNC machine tools

Contact Info

Product

Resources

About