Inferential statistics models to relate the rejections of an engine cold testing and the machining defects in camshaft assembly bores
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In the manufacturing industry, it is important to reduce machining deviations as soon as possible to avoid the cost
associated with reworks. The definition of mathematical models that predict future failures in the diagnosis of
combustion engines associated with errors in machining is a way that helps to save time and money in a process. This
paper proposes the analysis and establishment of correlations between the deviations of the machining in cylinder heads and the rejections of an engine cold testing in an automotive manufacturing company. To determine the relationships, a sample of heads and engines was measured in two months, and statistical models were established using inferential statistics. It was possible to establish 77 statistical models that allow predicting which machining of the cylinder heads are contributing to the rejects and therefore adjust the corresponding tools. Due to a large amount of data from the results of the 77 models, this article shows only one model which is one of the most representatives.
Using this statistical model, it was possible to know which characteristic of the tool should be adjusted in addition it
was also possible to know that the test limits for oil pressure have to be adjusted in the engine cold testing.
Dr. Mario Hernandez Nuñez, Advanced Technology Center CIATEQ, Toluca, México.
https://orcid.org/0000-0001-8178-3366
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