Crankshaft Journal Roundness Error Assessment Based on Improved K-means Clustering Centering Algorithm
The roundness error of the crankshaft journal is a core dimension that must be inspected on the crankshaft,which directly affects the life and performance of the crankshaft.In order to solve the problem of large amount of roundness error data and complex calculation,a roundness error assessment method based on the improved K-means clustering center-ing algorithm is proposed.This algorithm obtains the set UK by performing circular clustering on the sample points of the journal sampling channel.At the same time,the designed target controller is used to eliminate the noise points of UK,and the least square roundness evaluation error fm of UK is used to estimate the error of the entire circular sample.The clustering value increases iteratively from K=5 until fm meets the preset SQC rules.The evaluation results show that the roundness er-ror assessment method of the cluster centering algorithm can achieve efficient and accurate assessment of crankshaft round-ness error.
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