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Recent Advances in the Thermal Error of Precision Machine Tools
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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
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Recent Advances in the Thermal Error of Precision Machine Tools

Abstract submission deadline
closed (30 June 2024)
Manuscript submission deadline
31 October 2024
Viewed by
7491

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 		2.2 3.9 2012 16.5 Days CHF 2400 Submit
Energies
energies 		3.0 6.2 2008 17.5 Days CHF 2600 Submit
Machines
machines 		2.1 3.0 2013 15.6 Days CHF 2400 Submit
Robotics
robotics 		2.9 6.7 2012 17.7 Days CHF 1800 Submit
Sensors
sensors 		3.4 7.3 2001 16.8 Days CHF 2600 Submit

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Published Papers (3 papers)

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