Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map

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This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.

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Edited by:

Kesheng Wang, Jan Ola Strandhagen and Dawei Tu

Pages:

538-543

Citation:

L. Stupar et al., "Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map", Advanced Materials Research, Vol. 1039, pp. 538-543, 2014

Online since:

October 2014

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$38.00

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DOI: https://doi.org/10.1049/pbpo022e

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