Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

Abstract

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 Spot Welding. 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.

Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

“…Data Description. The dataset D is collected from a German factory, in which reside production lines that consist of 27 welding machines of an impactful automated welding process widely applied in automotive industry: the resistance spot welding [16,17]. D contains a high number of welding operation records and a series of welding sensor measurements.…”

Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

“…The domain ontology O is an OWL 2 ontology and can be expressed in Description Logics SH I (D). With its 1249 axioms, which contain 147 classes, 145 object properties, and 132 datatype properties, it models the general knowledge of a type of fully automated welding process, resistance spot welding [47,59,62,66],…”

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