Large language model based agent for process planning of fiber composite structures
Maximilian Holland, Kunal Chaudhari
Abstract
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.
Topics & Concepts
WorkflowProcess (computing)Computer scienceFiberENCODEAutomationA priori and a posterioriMotion planningComposite numberSystems engineeringDistributed computingIndustrial engineeringEngineeringArtificial intelligenceAlgorithmMechanical engineeringDatabaseProgramming languageMaterials scienceBiochemistryEpistemologyRobotPhilosophyChemistryGeneComposite materialManufacturing Process and OptimizationScheduling and Optimization AlgorithmsBIM and Construction Integration