Fraunhofer Institute for Casting
Assembly Line
Large language model based agent for process planning of fiber composite structures
Process planning is a crucial activity, connecting product development and manufacturing of fiber composite structures. Recently published Large Language Models (LLM) promise more flexible and autonomous workflows compared to state of the art automation methods. An autonomous agent for process planning of fiber composite structures is implemented with the LangChain framework, based on OpenAI’s GPT-4 language model. The agent is equipped with deterministic tools which encode a-priori process planning knowledge. It can handle different process planning problems, such as cycle time estimation and resource allocation. Combinations thereof are solved through executing a multi-step solution path.
The agent is supposed to solve these problems autonomously:
- Time Estimation - Estimate the cycle time, i.e., duration from start to end, for a manufacturing task.
- Process Chains - Determine which tasks are required in which order to manufacture a specific component.
- Resource Allocation - Identify the resources, e.g. machines, required to manufacture a specific component.
- Integrated Planning - Estimate the total cycle time for a chain of tasks required to manufacture a component.