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Bayesian hierarchical model for combining misaligned two-resolution metrology data

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  • Haifeng Xia
  • Yu Ding
  • Bani Mallick

Abstract

This article presents a Bayesian hierarchical model to combine misaligned two-resolution metrology data for inspecting the geometric quality of manufactured parts. High-resolution data points are scarce and scatter over the surface being measured, while low-resolution data are pervasive but less accurate and less precise. Combining the two datasets should produce better predictions than using a single dataset. One challenge in combining them is the misalignment existing between data from different resolutions. This article attempts to address this issue and make improved predictions. The proposed method improves on the methods of using a single dataset or a combined prediction that does not address the misalignment problem. Improvements of 24% to 74% are demonstrated both for simulated data of circles and datasets obtained for a milled sinewave surface measured by two coordinate measuring machines of different resolutions.

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  • Haifeng Xia & Yu Ding & Bani Mallick, 2011. "Bayesian hierarchical model for combining misaligned two-resolution metrology data," IISE Transactions, Taylor & Francis Journals, vol. 43(4), pages 242-258.
    • Handle: RePEc:taf:uiiexx:v:43:y:2011:i:4:p:242-258
    DOI: 10.1080/0740817X.2010.521804
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    Cited by:

    1. Romina Dastoorian & Lee J. Wells, 2023. "A hybrid off-line/on-line quality control approach for real-time monitoring of high-density datasets," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 669-682, February.

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