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A study of a rapid method for detecting the machined surface roughness

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Abstract

To achieve the surface roughness of the large-scale machined surface detection rapidly, a surface roughness detection method of “multi-dimensional feature parameters matrix + BP neural network algorithm + automatic acquisition of regions of interest” is proposed. A multi-dimensional feature parameters matrix containing the strong correlation coefficients related to the surface roughness is constructed by extracting various feature parameters of clean image based on the gray-level co-occurrence matrix. The BP neural network model is used to predict the surface roughness with multi-dimensional feature parameters matrix as input. The region of interest in the detected images is extracted consequently by gray value transformation, image filtering, and morphological methods, and the method of “multi-dimensional feature parameters matrix + BP neural network algorithm” is used to detect the surface roughness of the region of interest. The automatic surface roughness detection system is constructed by combining the proposed surface roughness detection methods to analyze the condition of the machined surface and give the surface roughness value rapidly. Compared with experiments, the detecting accuracy and efficiency of the developed system are evaluated regarding the different machined surface features. The results show that the relative errors between the clean image and the interfered image with chips are 6.41% and 5.46%, and the average value of a single detection time is not more than 1.15 s, which can meet the accuracy and time requirements of high-efficiency detection and provides certain technical support for the industrial automated detection.

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Availability of data and material:

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

Funding

This project was supported by the National Key Research and Development Project 2018YFB2002205), National Natural Science Foundation of China (No. 51875319), and Shandong Natural Science Foundation of China (ZR2020ZD05).

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Authors and Affiliations

  1. Centre for Advanced Jet Engineering Technologies (CaJET), School of Mechanical Engineering, Shandong University, Jinan, 250061, People’s Republic of China

    Wei Chen, Bin Zou, Yishang Li & Chuanzhen Huang

  2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan, People’s Republic of China

    Wei Chen, Bin Zou, Yishang Li & Chuanzhen Huang

  3. National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, People’s Republic of China

    Wei Chen, Bin Zou, Yishang Li & Chuanzhen Huang

Authors
  1. Wei Chen
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  2. Bin Zou
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  3. Yishang Li
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  4. Chuanzhen Huang
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Contributions

Wei Chen (first author): methodology, validation, formal analysis, investigation, writing original draft. Bin Zou (corresponding author): formal analysis, resources, writing—review and editing. Yishang Li, Chuanzhen Huang: formal analysis. The author’s contribution corresponds their order. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bin Zou.

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Not applicable. All data in this paper can be published and verified by all authors.

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The authors declare no competing interests.

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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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Cite this article

Chen, W., Zou, B., Li, Y. et al. A study of a rapid method for detecting the machined surface roughness. Int J Adv Manuf Technol 117, 3115–3127 (2021). https://doi.org/10.1007/s00170-021-07733-9

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  • DOI: https://doi.org/10.1007/s00170-021-07733-9

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