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Periodic trend detection from CMM data based on the continuous wavelet transform

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Abstract

The main goal of this paper is to describe a method based on the continuous wavelet transform (CWT). The Shannon entropy of CWT coefficients in each scale is calculated in order to detect the characteristic trends of the CMM measurement data efficiently. This method is successfully implemented in a body-in-white (BIW) assembly for the detection of periodic trends in coordinate measure machine (CMM) data. It would contribute to main cause location in BIW quality control. The principle of the CWT and the property of the periodic trend after the CWT are explained from a mathematical viewpoint. An actual quality-control case is analyzed using the CWT method. Consistency between the results and the actual situation proves the effectiveness of this CWT method.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200030, P.R. China

    Wang Hua, Chen Guanlong, Zhu Ping & Lin Zhongqin

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  1. Wang Hua
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  2. Chen Guanlong
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  3. Zhu Ping
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  4. Lin Zhongqin
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Correspondence to Wang Hua.

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Hua, W., Guanlong, C., Ping, Z. et al. Periodic trend detection from CMM data based on the continuous wavelet transform. Int J Adv Manuf Technol 27, 733–737 (2006). https://doi.org/10.1007/s00170-004-2232-2

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  • DOI: https://doi.org/10.1007/s00170-004-2232-2

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