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Ambient temperature-induced thermal error modelling for a special CMM at the workshop level based on the integrated temperature regression method

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Abstract

Coordinate measurement machines (CMMs) at the workshop level are gaining increasing significance for achieving high machining efficiency while ensuring machining accuracy. As thermal error significantly affects the measurement accuracy of CMMs at the workshop level, it is crucial to predict and compensate for this kind of error. Based on functional requirements, we investigated the thermal positioning error of the Z-axis of this special CMM. The main thermal error source is determined as the ambient temperature for the considered CMM running at a low measuring speed. Usually, the ambient temperature is used as a single variable. We believe that this is inaccurate, because when the temperature changes drastically, the temperature at different locations would be different owing to uneven heat transfer. Therefore, in the study, we split the ambient temperature into multiple temperature variables. This leads to a strong correlation among the variables and a reduction in the accuracy and robustness of the error model. Then, we developed an integrated temperature regression method for thermal error modelling. This method merged these temperature variables into one temperature variable for thermal error modelling based on error separation. Afterwards, the integrated temperature model is compared to the single temperature and ridge regression models. The results show that the proposed temperature regression method can eliminate the collinearity between input variables and simplify the overall thermal error modelling. In addition, the compensation accuracy of the proposed model can be controlled within 5 μm at a large ambient temperature range (10–35 °C) by using only two temperature sensors.

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All data generated or analyzed during this study are included in this published article.

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The codes required to reproduce these findings cannot be shared at this time as the codes also forms part of an ongoing study.

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Funding

This study was partially supported by the key research project of the Ministry of Science and Technology (Grant No. 2018YFB1306802) and the National Natural Science Foundation of China (Grant Nos. 51975344 and 52075337).

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Guangjie Jia, Jianbin Cao & Xu Zhang

  2. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Nuodi Huang

Authors
  1. Guangjie Jia
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  2. Jianbin Cao
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  3. Xu Zhang
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  4. Nuodi Huang
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Contributions

Guangjie Jia conducted experiments, data collection and analysis, and wrote the paper. Xu Zhang provided experimental materials and funds, and participated in the design of experiments. Nuodi Huang provided financial support and revision of the paper. Jianbin Cao participated in the result analysis and data processing.

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Correspondence to Xu Zhang.

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Jia, G., Cao, J., Zhang, X. et al. Ambient temperature-induced thermal error modelling for a special CMM at the workshop level based on the integrated temperature regression method. Int J Adv Manuf Technol 121, 5767–5778 (2022). https://doi.org/10.1007/s00170-022-09533-1

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  • DOI: https://doi.org/10.1007/s00170-022-09533-1

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