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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.

The current work addresses an industrial problem related to injection moulding manufacturing with focus on mould wear-out prediction. Real data sets are provided by an industrial partner that uses a multitude of moulds with different shapes and sizes in its production. An analysis of the data is presented and begins with clustering the moulds based on their characteristics and pre-chosen running settings. Using the results of the clustering, the mould wear-out is modelled using Kaplan-Meier survival curves. Furthermore, a random survival forest model is fitted for comparison and model performance is assessed. The main novelty of the case study is the implementation of mould wear-out prediction in real-time with the outcomes presented in terms of conditional survival curves including a proposed early warning system. For visualization and further industrial implementation, a R Shiny dashboard is developed and presented.

Various production/process equipment's have built-in sensors allowing for continuous collection of process data. However to ease the data processing burden, it is often the case that only certain features such as aggregated measures or peak values are stored. Yet also in some cases such sensor signals can be extracted fully reflecting the dynamics of the process and utilized for process optimization. The aim of this paper is to demonstrate that such data can be utilized in an effective way to optimize an injection molding process using signals from built-in pressure and position sensors. The observational process data is combined with data from controlled experiments to observe the causal relationships between disturbance factors, process settings and the final quality of the products. We demonstrate that signals from built-in injection molding machine sensors can be used for detecting and mitigating quality issues caused by variation in raw material due to the dual sourcing from two suppliers, which cannot actually be identified during production. For this, we show that the origin of raw material can be classified using the time series profiles of dosing pressure and PLS-DA (Partial Least Squares Discriminant Analysis). Through experimental work, we conclude that this classification can be used for increasing the operating window for holding pressure and mold temperature, which ensure production of products within specifications.

Manufacturing has been rejuvenated by automation and digitalization. This has brought forth the new industrial era also called Industry 4.0. During the last few years we have collaborated with companies from various industries that have all been going through this transformation. Through these collaborations, we have collected numerous examples of (sometimes troublesome) experiences with Big Data applications of production analytics. These experiences reflect the current state of production data and the challenges it poses. Our goal in this paper is to share those experiences and lessons learned in dealing with practical issues from data acquisition to data management and finally to data analytics.

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