Current assembly systems are handling the increased requirements for mass customization with difficulties and need to be updated with new approaches and technologies. Cyber-Physical Systems (CPS) auto-configuration is regarded as an important asset towards automation components, which autonomously embed themselves into the system. In this context, knowledge-based technologies pave the way for highly flexible and reconfigurable CPS. This paper introduces and demonstrates a model-driven engineering approach for automatically configuring the control layer of a CPS based on knowledge representation of the environment and component capabilities. The approach encompasses a control architecture that is tested in two industrial use cases. The first case employs a configuration infrastructure for control software based on IEC 61499 to automatically configure the hardwarenear control layer of a CPS within an assembly line. The second case is concerned with autonomously generating assembly plans, which are then transformed into actions that an industrial robot sequentially executes.

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Additive Manufacturing (AM) evolved recently from a rapid prototyping process to a standard manufacturing tool. Nevertheless, it is still not a widely used method due to different process-related challenges. In recent years printer technologies and possible printable materials emerged but there are still challenging demands on the printing process. Hence, it is of vital importance to inspect the manufacturability of the designed parts. This work focuses on the not yet widely researched ceramic printing with the Lithography-based Ceramic Manufacturing (LCM) processes. It presents a knowledge-driven framework able to automatically examine geometric properties of a part and compare it to AM guidelines. As a knowledge base, an ontology is used which contains information about the capabilities of AM processes, printers and materials. The manufacturability system uses triangle-based mesh processing algorithms to recognize features and check the guidelines necessary for LCM. The evaluation shows the feasibility of manufacturability analysis with the developed framework and its limitations. INDEX TERMSAdditive manufacturing, computer-aided design, manufacturability analysis, knowledge representation, ontology.

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