Cristin-resultat-ID: 1993866
Sist endret: 9. mars 2022, 16:26
NVI-rapporteringsår: 2022
Resultat
Vitenskapelig artikkel
2022

Machine Learning with Domain Knowledge for Predictive Quality Monitoring in Resistance Spot Welding

Bidragsytere:
  • Baifan Zhou
  • Tim Pychynski
  • Markus Reischl
  • Evgeny Kharlamov og
  • Ralf Mikut

Tidsskrift

Journal of Intelligent Manufacturing
ISSN 0956-5515
e-ISSN 1572-8145
NVI-nivå 2

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2022

Importkilder

Scopus-ID: 2-s2.0-85125531475

Beskrivelse Beskrivelse

Tittel

Machine Learning with Domain Knowledge for Predictive Quality Monitoring in Resistance Spot Welding

Sammendrag

Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance SpotWelding (RSW). Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

Bidragsytere

Baifan Zhou

  • Tilknyttet:
    Forfatter
    ved SIRIUS - Senter for Innovasjon ved Universitetet i Oslo

Tim Pychynski

  • Tilknyttet:
    Forfatter

Markus Reischl

  • Tilknyttet:
    Forfatter

Evgeny Kharlamov

  • Tilknyttet:
    Forfatter
    ved SIRIUS - Senter for Innovasjon ved Universitetet i Oslo

Ralf Mikut

  • Tilknyttet:
    Forfatter
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