Abstract
ABSTRACTCurrent 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 hardware-near 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.
Highlights
Production systems for the manufacturing of goods are often large complex systems containing numerous different subsystems
Cyber-Physical Systems (CPS) auto-configuration is regarded as an important asset towards automation components, which autonomously embed themselves into the system
Thereby, an executable level control (LLC) implementation is generated for each CPS component within the corresponding controller
Summary
Production systems for the manufacturing of goods are often large complex systems containing numerous different subsystems. As most CPS involve the cooperation of a high number of components (Kang, Kapitanova, & Son, 2012), explicitly programming the relationships between the system components and considering the quantity of interrelationships related to failures poses significant challenges (Wang & Lin, 2009) In this context, automatic configuration is proposed as a reasonable means for making a system scalable and robust in the presence of changes and for supporting dynamic adaptation (Kramer & Magee, 2007). The CP component that is controlling the robot, generates the action sequences based on the semantic descriptions of the product and reason how a product should be assembled as well as in which manner parts should be moved from one position to another Both cases are implemented and demonstrated on real equipment in a laboratory environment.
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