Plastic Extrusion Process Optimization by Inversion of Stacked Autoencoder Classification Machines

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.