Abstract
Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples.
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10479_2022_5077_MOESM1_ESM.gif
In the online supplementary materials of this paper, the files “cylinder pattern 1.gif” and “cylinder pattern 2.gif” show animated visualizations of individual patterns of the two discovered variation sources in the cylindrical example in Section 4, along Visualization paths #1 and #2 in Fig. 4, respectively. Similarly, the files “gasket bead pattern 1.gif”, “gasket bead pattern 2.gif”, and “gasket bead pattern 3.gif” show animated visualizations of individual patterns of the three discovered variation sources in the gasket bead example in Section 5, along Visualization paths #1, #2, and #3 in Fig. 7, respectively. Supplementary file1 (GIF 461 KB)
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Bui, A.T. Root cause analysis of manufacturing variation from optical scanning data. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-05077-5
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DOI: https://doi.org/10.1007/s10479-022-05077-5