PyAML: Enhancing AutomationML for Advanced Virtualization of Industry 4.0 Cyber-Physical Production Systems with Python Code Injections

Industry 4.0 production systems should provide flexibility with respect to products as well as their corresponding production processes under consideration of the availability of production resources. Such production systems consist of inter-linked components including robots in robotic work cells, transportation systems, processing technologies, etc. The shift from a more static, traditionally engineered, manufacturing system towards Industry 4.0 requires increasingly sophisticated, yet generic system components, leading to an overall increase in complexity of integration and testing. In this work, we contribute to the task of system integration by utilizing a centralized configuration based on the industry standard AutomationML, explaining how to efficiently design, use, and incorporate a digital twin including the corresponding virtualization of components' interfaces. To do these tasks efficiently, we are enhancing the Au-tomationML data format with Python script injections, in order to represent needed information patterns easily and efficiently. The entire proposed approach is demonstrated and evaluated on our internal Industry 4.0 Testbed.