Time series data for process monitoring in injection molding: a quantitative study of the benefits of a high sampling rate

Abstract

Abstract Process monitoring systems are playing an increasingly important role in reducing production capacity losses in injection molding. Process monitoring and optimization systems are mostly based on processing data of injection molding machine control systems. These data consist of scalar data and time series. This paper introduces a novel approach to modelling injection molding processes using only time series data and evaluates the quantitative influences of varying sampling times on calculation of integral values and model quality. On the basis of the first experiment, it is shown that the sampling rates of these time series have a large influence on information which can be derived from this data (e.g. injection work). These findings provide an assessment of whether the effort is justified for the respective requirements on the accuracy of the injection work and other parameters derived from the time series. In the second experiment, a model is presented which uses only the injection flow and injection pressure profile as input and achieves high coefficients of determination for the prediction of the part weight, despite the absence of mold sensor data and scalar data. It is shown that higher sampling rates of time series results in higher prediction quality of these models. This improves the understanding of the data needed for high quality machine learning models of injection molding processes and enable users to estimate a lower bound for the sample rates of time series for their use cases.