Abstract
The proposed paper focuses on utilization of neural networks for paint errors classification in the area of automotive industry. The paper utilizes hypothesis, that outdoor weather has significant impact on the number of paint errors, as a basis for comparison of neural network algorithms. For the neural network algorithms comparison we used real production data from the paint shop process. The paper deals also with definition of classification classes and attributes selection, as well as the data integration process itself that utilizes Hadoop platform as an intermediate data storage.
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Acknowledgments
This publication is the result of implementation of the project VEGA 1/0272/18: “Holistic approach of knowledge discovery from production data in compliance with Industry 4.0 concept” supported by the VEGA.
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Kebisek, M., Spendla, L., Tanuska, P., Gaspar, G., Hrcka, L. (2019). Neural Network Comparison for Paint Errors Classification for Automotive Industry in Compliance with Industry 4.0 Concept. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_35
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DOI: https://doi.org/10.1007/978-3-030-19810-7_35
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