Topic Editors

Dr. Chi Ma
1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China
Dr. Hu Shi
School of Mechanical Engineering, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an 710049, China
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Institute of Manufacturing Technology & Equipment Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Dr. Weiguo Gao
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
Dr. Sitong Xiang
Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China

Recent Advances in the Thermal Error of Precision Machine Tools

Abstract submission deadline
30 June 2024
Manuscript submission deadline
31 October 2024
Viewed by
6122

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

  • machine tool
  • precision machine tool
  • spindle system
  • feed drive system
  • rotary axis
  • linear axis
  • thermal behavior
  • thermal analysis
  • thermal simulation
  • thermal characteristics
  • temperature rise
  • thermal deformation
  • thermal information
  • data-driven modeling and prediction
  • simulation-driven modeling
  • error modeling
  • time-series prediction
  • error prediction model
  • thermal error
  • thermal error compensation
  • compensation method
  • thermal error prediction
  • dynamic error
  • fuzzy clustering
  • gray clustering
  • recurrent neural network
  • artificial intelligence
  • deep learning
  • correlation analyses
  • control system
  • intelligent system
  • system framework
  • error compensation system
  • real-time system
  • expert system
  • knowledge-based system

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

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Published Papers (3 papers)

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18 pages, 3852 KiB  
Article
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 1183
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
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15 pages, 5186 KiB  
Article
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 2188
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
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10 pages, 2753 KiB  
Article
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 1289
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
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