Diagnostics of Product Defects by Clustering and Machine Learning Classification Algorithm

Kamil Židek^1, and Vladislav Maxim²

¹Faculty of Manufacturing Technologies with a seat in Presov, Presov, Slovak Republic

²Institute of Automation, Robotics and Mechatronics, FME, Technical University of Kosice, Slovak Republic

Cite this paper:
Kamil Židek and Vladislav Maxim. Diagnostics of Product Defects by Clustering and Machine Learning Classification Algorithm. Journal of Automation and Control. 2015; 3(3):96-100. doi: 10.12691/automation-3-3-11

Abstract

The article deals with usability of clustering and machine learning classification algorithm for search systematic surface errors. The main idea is to propose a methodology for the automated identification, diagnostics and localization of systematic errors in mass production. The introduced methodology consists of three levels: image processing for error parameterization, clustering for creating of errors classes (teach data) and prediction of new samples by machine learning algorithm. We conducted experiments with density based clustering algorithm DBSCAN. For classification we use multilayer perceptron MLP/ANN.

Keywords:
inspection clustering machine learning image processing

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