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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.

Real-time adjustment of injection molding process settings by utilizing Design of Experiment, time series profiles and PLS-DA