An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Abstract

This work focuses on the effective utilisation of varying data sources in injection moulding for process improvement through a close collaboration with an industrial partner. The aim is to improve productivity in an injection moulding process consisting of more than 100 injection moulding machines. It has been identified that predicting quality through Machine Process Data is the key to increase productivity by reducing scrap. The scope of this work is to investigate whether a sufficient prediction accuracy (less than 10% of the specification spread) can be achieved by using readily available Machine Process Data or additional sensor signals obtained at a higher cost are needed. The latter comprises Machine Profile and Cavity Profile Data. One of the conclusions is that the available Machine Process Data does not capture the variation in the raw material that impacts element quality and therefore fails to meet the required prediction accuracy. Utilising Machine Profiles or Cavity Profiles have shown similar results in reducing the prediction error. Since the cost of implementing cavity sensors in the entire production is higher than utilising the Machine Profiles, further exploration around improving the utilisation of Machine Profile Data in a setting where process variation and labelled data are limited is proposed.

Deep learning applications in manufacturing operations: a review of trends and ways forward

“…, 2021), cold chain logistics (Lim et al. , 2021), injection molding (Rønsch et al. , 2021), augmented reality (Sahu et al.…”

“…, 2020). In this regard, manufacturers are intrigued with the idea of leveraging cutting-edge advances such as DL, which opens up new opportunities for Internet of things (IoT) based machineries to understand the processes involved, interact with their surroundings, and change their behavior in response to arising operational issue or situation (Rønsch et al. , 2021; Sahu et al.…”

Machine Learning Approaches, Technologies, Recent Applications, Advantages and Challenges on Manufacturing and Industry 4.0 applications

“…The suggested method accurately predicts the line feeding mode with respect to categorization. Rnsch, Kulahci, and Dybdahl reveal their research on the study that examines the efficient usage of data from different sources for process improvement in injection moulding in their article titled "An investigation of the utilisation of different data sources in manufacturing with application in Injection Moulding" [41]. The researchers specifically examine regardless of wether additional sensor signals obtained at a greater price might provide more useful data but whether a high predictive accuracy can sometimes be attained by using widely accessible Machine Process Data on a moulding manufacturing line consisting of 100 injection moulding machines.…”

“…They reached the conclusion that perhaps the variability inside the raw resources that affects element quality is not captured by the machine data processing for the specific use case that was carried out in close cooperation with an industrial partner. The research "Using process mining to increase productivity in make-to-stock manufacturing" by Lorenz et al [41] demonstrates an unique application case using datadriven method to improve productivity in make-tostock industrial production. In particular, the described approach makes use of process mining to automatically dynamically map and analyse industrial processes with high levels of complexity and variation.…”

Machine learning in manufacturing and industry 4.0 applications

“…'An investigation of the utilisation of different data sources in manufacturing with application in Injection Moulding' by Rønsch et al (Rønsch, Kulahci, and Dybdahl 2021) report their work on the study that explores effective utilisation of various data sources for process improvement in injection moulding. Specifically, the authors investigate whether a good prediction accuracy can be achieved by using readily available Machine Process Data on a moulding manufacturing line comprising of 100 injection moulding machines or additional sensor signals obtained at a higher cost can provide additional beneficial information.…”