Fraunhofer Institute for Cognitive Systems IKSAutomated systems typically exist in a very dynamic context. For instance, unexpected events in manufacturing, such as the sudden arrival of new orders and machine outages, are common challenges that require swift and flexible solutions. Thus, their quick and reliable assessment is critical for the success of the business.
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Adaptive and Optimized Behavior
Challenge: Responding to the Environment
- Responding to changes in the environment is not always easy: Planned events, such as regular maintenance, and unplanned events, like the failure of a machine, can both occur at any time. Manufacturing systems should also support just-in-time production and respond to short-term orders in an adaptive and optimized way.
- In addition, this level of flexibility must be balanced against the space and cost constraints of the shop floor, as there cannot be a machine for every variation.
- Balancing these requirements with ongoing production is critical to maintaining customer satisfaction and profitability.
Need: An Adaptive Behavior Assessment System
- To meet these challenges, it is necessary to maximize the use of production machinery while minimizing the cost of reconfiguring expensive equipment on the shop floor.
- Some of the key aspects to consider are the ability to minimize reconfiguration costs and errors without downtime.
- Furthermore, the production output should be maximized while maintaining a high level of quality for the resulting goods, as well as complying with required safety standards.
Solution: Adaptive Optimization and Framework
An adaptive behavior framework can address these concerns by managing, monitoring, and modifying system processes. It supports the automation of changes and provides solutions to dynamically reconfigure software. In addition, detailed shop floor modelling and effective optimization techniques can be used to improve the reconfiguration processes as much as possible. This increases the overall quality and reliability of production systems.