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Identifying failure root causes by visualizing parameter interdependencies with spectrograms
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DOI：10.1016/j.jmsy.2019.08.002.png)
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Abstract
En 中文
Fast identification of failure root causes is a major task in minimizing rejects in manufacturing. Due to increasing complexity of products and supply chains many interdependencies affect the final product quality. As knowledge about every possible interdependence is hardly held by individuals, data analysis strategies are required to evaluate captured information. Hence, we propose a root cause identification method for determining the main influencing factors on end products failing end of line tests. Additionally, graphical representation of calculation results in spectrograms similar to audio signals facilitates human interpretation. The evaluation of the proposed method on the basis of a use case proves the applicability in a real scenario. The method is able to identify the root cause for rejects within short periods of time. In this specific case it shortened the analysis time by a factor of about 50. In the future, it empowers smart production systems to automatically identify failure root causes and to take countermeasures like adjusting process parameters.
Keywords:
Quality management
Root cause analysis
Scrap reduction
Visual management
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