A Prestudy of Machine Learning in Industrial Quality Control Pipelines

Jože Ravničan, Anže Marinko, Gjorgji Noveski, Stefan Kalabakov, Marko Jovanovič, Samo Gazvoda, Matjaž Gams


Today’s fast paced industrial production requires automation at
multiple steps during its process. Involving humans during the
quality control inspection provides high degree of confidence that
the end products are with the best quality. Workers involved in
the control process may have an impact on production capacity
by lowering the throughput, depending on the complexity of the
control process at the time the control is carried out, during the
process which is a time-critical operation, or after the process is
completed. Companies are striving to fully automate their quality
control stages of production and it comes naturally to focus on
using various machine learning methods to help build a quality
control pipeline which will offer high throughput and high degree
of quality. In this paper we give an overview of applying several
machine learning approaches in order to achieve an autonomous
quality control pipeline. The applications for these approaches
were used to help improve the quality control pipeline of two of
the biggest manufacturing companies in Slovenia. One of the most
challenging part of the study was that the tests had to be performed
only on a small number of defective products, as is in reality. The
motivation was to test several methods to find the most promising
one for later actual application.

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DOI: https://doi.org/10.31449/inf.v46i2.3938

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