Thermal Error Modeling of Rotary Axis Based on Convolutional Neural Network

Wu, Chengyang; Xiang, Sitong; Wansheng, Xiang

doi:10.1115/1.4049494

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Williams et al [18] used a 3D CNN to estimate quantitative manufacturing metircs like part mass, support materials mass or build time from voxelbased component geometries. Wu et al [19] employed CNN to precisely model the thermal error of rotary axis based on thermal imager. The Recurrent neural network (RNN), especially its successful variant long short-term memory (LSTM), have shown its advantage in modeling sequential data [20].…”

Section: Related Workmentioning

confidence: 99%

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Xiong

2022

Preprint

View full text Add to dashboard Cite

Determining reasonable tolerance patterns requires much knowledge and experience to balance the conflicting requirements. In previous studies, the process of tolerance specification has been based on the engineer’s experience and empirical data handed down from earlier projects. This paper proposes a learning model of tolerance specification of shaft part using deep learning architecture named CNN-BiLSTM-CRF, which is a combination of a Convolutional Neural Network, BiDirection Long Short-Term Memory network, and Conditional Random Field model. The CNN layer extracts the features from the CAD model; the Bi-LSTM layer composes the nonlinear relationship between tolerance specification and the features embedded by the CNN layer; the CRF layer predicts the tolerance sequence of the whole shaft part by jointly considering relations between neighbor tolerances. The proposed method directly takes the CAD model as the input and learns the tolerance specification from previous successful cases. Therefore, it does not require engineers to have any empirical knowledge.

show abstract

Section: Related Workmentioning

confidence: 99%

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Xiong

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, data may not start from time zero, and excessively long data segments could risk information loss. Wu et al [23] utilized a thermal imager to directly acquire two-dimensional thermal image data containing rich information about the rotational axis, establishing a CNN prediction model. Zou et al [24] collected multi-source signal data, including spindle current and temperature signals, and synchronized and trimmed these data.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Liu [17,18] leveraged the long-term memory properties of LSTM neural networks to establish thermal error models, allowing multiple time data points to contribute to thermal error prediction and enhancing prediction accuracy. Additionally, some researchers [19] have based their work on convolutional neural networks, using deep learning to directly establish mathematical models between the overall machine tool temperature field and thermal errors.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Prediction of Thermal Deformation Errors in Fiber Optic Gyroscopes Based on the TD-Model

Xu,

Tian,

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

For a fiber optic gyroscope, thermal deformation of the fiber coil can introduce additional thermal-induced phase errors, commonly referred to as thermal errors. Implementing effective thermal error compensation techniques is crucial to addressing this issue. These techniques operate based on the real-time sensing of thermal errors and subsequent correction within the output signal. Given the challenge of directly isolating thermal errors from the gyroscope’s output signal, predicting thermal errors based on temperature becomes necessary. To establish a mathematical model correlating the temperature and thermal errors, this study measured synchronized data of phase errors and angular velocity for the fiber coil under various temperature conditions, aiming to model it using data-driven methods. However, due to the difficulty of conducting tests and the limited number of data samples, direct engagement in data-driven modeling poses a risk of severe overfitting. To overcome this challenge, we propose a modeling algorithm that effectively integrates theoretical models with data, referred to as the TD-model in this paper. Initially, a theoretical analysis of the phase errors caused by thermal deformation of the fiber coil is performed. Subsequently, critical parameters, such as the thermal expansion coefficient, are determined, leading to the establishment of a theoretical model. Finally, the theoretical analysis model is incorporated as a regularization term and combined with the test data to jointly participate in the regression of model coefficients. Through experimental comparative analysis, it is shown that, relative to ordinary regression models, the TD-model effectively mitigates overfitting caused by the limited number of samples, resulting in a substantial 58% improvement in predictive accuracy.

show abstract

Thermal Error Modeling of Rotary Axis Based on Convolutional Neural Network

Cited by 10 publications

References 19 publications

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

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

Modeling and Prediction of Thermal Deformation Errors in Fiber Optic Gyroscopes Based on the TD-Model

Contact Info

Product

Resources

About