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

Baifan Zhou,Tim Pychynski,Markus Reischl,Ralf Mikut,Evgeny Kharlamov

doi:10.1007/s10845-021-01892-y

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.

Highlights

Technological advances in trends of Industry 4.0 (Kagermann 2015) and Internet of Things (ITU 2012), including technologies in sensoring, communication, information processing, and actuation, have opened horizons of new opportunities and challenges to change the paradigm of many industrial processes, such as manufacturing, oil and Eggenstein-Leopoldshafen, Germany 3 SIRIUS Centre, University of Oslo, 0316 Oslo, Norway 4 Bosch Center for Artificial Intelligence, 71272 Renningen, Germany gas, chemical and process industries
1 Note that quality monitoring that we investigate here falls into a wider well-known category of condition monitoring that is typically divided into two categories machine health monitoring (Zhao et al 2019) that aims at maintaining the healthy state of an equipment, and quality monitoring that aims at ensuring the product quality within acceptable limits
The results show that the selected features are largely overlapping with those selected by Linear regression (LR)

Summary

Introduction

Technological advances in trends of Industry 4.0 (Kagermann 2015) and Internet of Things (ITU 2012), including technologies in sensoring, communication, information processing, and actuation, have opened horizons of new opportunities and challenges to change the paradigm of many industrial processes, such as manufacturing, oil and Eggenstein-Leopoldshafen, Germany 3 SIRIUS Centre, University of Oslo, 0316 Oslo, Norway 4 Bosch Center for Artificial Intelligence, 71272 Renningen, Germany gas, chemical and process industries. Kagermann describes Industry 4.0 in the way that smart machines, storage systems and production facilities will be incorporated into aggregate solutions that are often referred to as Cyber-Physical Systems (Kagermann 2013; NSF 2010). Such systems are integrated in smart factories. The machines have control units that monitor and process the data, coordinate machines and manufacturing environment and send messages, notifications, requests. Such data generated during manufacturing (Chand and Davis 2010; Wuest et al 2016) has led to a large growth of interest in data analysis for a wide range of industrial applications (Mikhaylov et al 2019a, b; Zhou et al 2017, 2019)

Methods

Results

Conclusion