With growing customer demands for product customization, smaller batches, and tighter delivery times, manufacturing companies need a production infrastructure that is as adaptable as possible. Fraunhofer IKS helps to bring these adaptation capabilities into production, using modular mechanisms that improve how resources respond to sudden changes. This increases the flexibility and resilience of the shop floor.
Manufacturing companies are turning to digitalization and automation to remain competitive in a demanding market. The shift towards these new technologies enables the production line to dynamically adapt, e.g., to stay productive despite machine failures, or to cater to changing production processes. However, to fully make use of these new capabilities, companies need to adapt their production resources dynamically. This requires updating their infrastructure to include mechanisms such as monitoring, AI-based decision making, etc. It can be difficult to incorporate adaptation into well-established production lines, but these new capabilities are essential for the long-term success of the business.
To bring self-adaptation into manufacturing systems without disrupting the existing infrastructure, Fraunhofer IKS is working on a toolbox of mechanisms to increase the flexibility and resilience of production. These consist of modular add-ons that can be seamlessly integrated with the existing system. For instance, if the focus is on responding to unexpected machine failures, a scheduling add-on could be integrated into the system. This module would automatically generate alternative schedules, providing suitable options to react to a failure.
To implement AI techniques that enable adaptation, Fraunhofer IKS breaks the problem up in terms of an adaptation loop that consists of four stages, each with its own tool specific tools:
We provide you with a toolbox that can make production more flexible and resilient along the stages of the adaptation loop.
An example use case that can be tackled by the toolbox is the implementation of an AI assistant to help users understand the state of a production line and request modifications. To achieve this, three mechanisms can be combined:
With these tools in place, any issues arising during production can be more quickly addressed, thus keeping the manufacturing system functioning as smoothly as possible.
Fraunhofer Institute for Cognitive Systems IKS