Recent Advances in the Thermal Error of Precision Machine Tools

Abstract submission deadline

30 June 2024

Manuscript submission deadline

31 October 2024

Viewed by

6097

Topic Information

Dear Colleagues,

In the last few years, the machining of complex precision parts has continuously increased to meet the requirements for the machining accuracy of machine tools. In this context, the machining accuracy of precision machine tools is becoming higher and higher. Geometric, thermal, and forced-induced errors are the main reason for the decrease in the machining accuracy of precision machines. The thermal error accounts for 40% to 70% of the total machining error, and thermal error caused by thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. Moreover, the higher the precision of the machine tool, the greater the proportion of the thermal error in the total machining error is. To improve the geometric precision of the machined complex parts, the thermal error should be reduced. Simulation-driven and data-driven methods are used to establish thermal error models. The simulation-driven method can conduct the thermal analysis of precision machine tools to obtain the temperature field, stress field, and thermal deformation of the whole machine tool and the function components. Then, some suggestions and optimizations are provided in the design stage. Data-driven methods are used to predict thermal error prediction, and the data-driven thermal error model is embedded into the error compensation system to artificially create a compensation component equal to the size of the thermal error and opposite in the direction of the thermal error. The error compensation is a practical and efficient method to reduce the thermal error. The accurate modeling and prediction of the thermal error is pivotal because the effectiveness of the compensation is directly determined by the accuracy and robustness of the thermal error model. In this regard, the title of this Topic is “Recent Advances in the Thermal Error of Precision Machine Tools”. The aim of this Topic is to attract original and innovative works but also review articles that cover the latest advances in the thermal behavior simulation as well as thermal error modeling, prediction, and compensation. We look forward to and welcome your participation in this Topic.

Dr. Chi Ma
Dr. Hu Shi
Dr. Fuxin Du
Prof. Dr. Kuo Liu
Dr. Zhengchun Du
Dr. Weiguo Gao
Dr. Sitong Xiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.6	3.2	2012	16.7 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Machines machines	2.6	2.1	2013	15.6 Days	CHF 2400	Submit
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Actuators Energies Machines Robotics Sensors

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

18 pages, 3852 KiB  

Open AccessArticle

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

by Xinyuan Wei, Honghan Ye, Jinghuan Zhou, Shujing Pan and Muyun Qian

Sensors 2023, 23(10), 4916; https://doi.org/10.3390/s23104916 - 19 May 2023

Cited by 4 | Viewed by 1180

Abstract

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack [...] Read more.

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method. Full article

(This article belongs to the Topic Recent Advances in the Thermal Error of Precision Machine Tools)

►▼ Show Figures

Figure 1

15 pages, 5186 KiB  

Open AccessArticle

An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing

by Liang Dong, Chensheng Wang, Guang Yang, Zeyuan Huang, Zhiyue Zhang and Cen Li

Sensors 2023, 23(3), 1240; https://doi.org/10.3390/s23031240 - 21 Jan 2023

Cited by 7 | Viewed by 2177

Abstract

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was [...] Read more.

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor. Full article

(This article belongs to the Topic Recent Advances in the Thermal Error of Precision Machine Tools)

►▼ Show Figures

Figure 1

10 pages, 2753 KiB  

Open AccessArticle

Temperature-Sensitive Points Optimization of Spindle on Vertical Machining Center with Improved Fuzzy C-Means Clustering

by Hu Shi, Qiangqiang Qu, Yao Xiao, Qingxin Liu and Tao Tao

Machines 2023, 11(1), 80; https://doi.org/10.3390/machines11010080 - 09 Jan 2023

Viewed by 1285

Abstract

The heat generated by motors and bearings of machine tools has a significant impact on machining accuracy. Error modeling and compensation has proven to be effective ways to reduce thermal errors and improve accuracy. An improved fuzzy c-means (FCM) clustering algorithm is proposed [...] Read more.

The heat generated by motors and bearings of machine tools has a significant impact on machining accuracy. Error modeling and compensation has proven to be effective ways to reduce thermal errors and improve accuracy. An improved fuzzy c-means (FCM) clustering algorithm is proposed to determine the optimized temperature sensitive points for thermal error modeling of a spindle on the vertical machining center. The sensors are deployed to measure the temperature of different positions of machine tools, and the improved FCM algorithm is used to classify the measured data. Combined with the F-test statistics of multiple linear regression, the optimal temperature points of each group are selected. The improved FCM clustering algorithm significantly reduces the multicollinearity problem among temperature measuring points and avoids them falling into local optimization. The modeling method was verified through experiments on two types of vertical machining centers. The results show that the accuracy of the spindle in Y and Z directions of the machine tools was increased by more than 75%, and the model has good robustness, demonstrating application prospects in the selection of temperature measuring points of the spindle system of vertical machining centers. Full article

(This article belongs to the Topic Recent Advances in the Thermal Error of Precision Machine Tools)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3