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An image-based quantitative model of surface measurement complexity for on-machine measurement

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Abstract

With the on-machine measurement (OMM) technology, the quality of curved workpieces can be measured directly on the machine after computer numerical control machining. Aiming at the problem that the complexity of surface measurement in traditional methods is only judged by subjective experience and is difficult to calculate, an image-based surface measurement complexity (SMC) quantitative model is proposed. First, according to the curved surfaces’ characteristics during the OMM process, the concept of SMC is introduced by analyzing several key factors. Then, the curvature and smoothness information of three-dimensional surfaces is converted into two-dimensional images’ information by using the conformal mapping with dimensionality reduction. Next, based on the image color and texture complexity calculation, a mathematical model combined with the area and profile correction coefficients is established. Finally, the SMC model’s validity is verified by a set of design-machining-inspection experiments on curved surfaces, and the relevant laws between the SMC and measurement efficiency and measurement accuracy are presented.

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Acknowledgements

The authors also would like to sincerely thank the reviewers for their valuable comments on this work.

Funding

This research was supported by the National Key Technology Research and Development Program of China (no. 2016YFB1101701) and the major scientific and technological project of Hubei Province, China (no. 2021AAA007).

Author information

Authors and Affiliations

  1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China

    Gaocai Fu, Buyun Sheng, Yingkang Lu, Ruiping Luo & Yuzhe Huang

  2. School of Mechanical Engineering, Hubei University of Technology, Wuhan, China

    Buyun Sheng

  3. Hexagon Manufacturing Intelligence (Qingdao) Co., Ltd, Qingdao, China

    Ganlin Sheng

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  1. Gaocai Fu
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Contributions

All authors contributed to the study of this paper. Gaocai Fu: conceptualization, methodology, software, writing — original draft. Buyun Sheng: supervision, writing — review and editing, funding acquisition. Yingkang Lu: investigation, visualization. Ruiping Luo: visualization, writing — review and editing. Ganlin Sheng: formal analysis, resources. Yuzhe Huang: formal analysis, investigation.

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Correspondence to Buyun Sheng.

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Fu, G., Sheng, B., Lu, Y. et al. An image-based quantitative model of surface measurement complexity for on-machine measurement. Int J Adv Manuf Technol 124, 1473–1490 (2023). https://doi.org/10.1007/s00170-022-10585-6

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