Continuous plastics compounding processes are highly complex from a physicochemical point of view and correspondingly difficult to optimize. Model-based digital twins suitable for this purpose require complex simulation approaches, trained personnel and input variables that are difficult to determine. However, data-based digital twins, which are in principle suitable for this purpose, often fail because of the enormous trial effort which is required to generate a sufficiently large database. To overcome this problem this paper describes a hybrid approach for generating the necessary database for a data-based digital twin. By intelligent combination of real experiments, adaptation of the physical-chemical process model to these few experimental data and subsequent data cloud generation with the adapted process model results in a sufficient data base for the digital twin.
In the face of climate change and rising energy prices, lowering energy usage of industrial machines is gaining widespread attention. Αpropriate machine settings could lead to reduced production costs and lower environmental impact, while simultaneously maintaining products' quality. However, defining the complex, nonlinear dependencies between these settings and energy usage or quality in manufacturing is often a challenging task. In the presented work, a method for optimized machine settings recommendation is proposed using inverse classification via autoencoders. The algorithm can suggest operation parameters, based on predefined intervals of energy consumption and product properties. The performance is evaluated on data generated by a digital twin of an extrusion process.
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