Retrieval-Augmented Generation using Knowledge Graphs for Manufacturing Problem-Solving

The increasing variety of products has resulted in a rise in production errors, primarily due to more complexity in manufacturing processes. This paper proposes a data-driven inline problem-solving approach to mitigate the response times associated with these errors. Problem-solving is initiated by detecting anomalies within processes by an autoencoder model. Upon identifying these anomalies, the proposed approach employs causal inference using a Failure Mode and Effects Analysis (FMEA)-based Bayesian Network (BN) to determine potential root causes. The inferred causes, along with the user's problem description, are processed within a hybrid Retrieval-Augmented Generation (RAG) framework. The RAG produces two sets of retrievals: one by querying a Knowledge Graph (KG) containing historic eight discipline-based (8D) problem-solving data to extract failure information and relationships; the other through keyword similarity and vector search techniques. The combined retrievals, along with the results from the BN, are then input for a relatively small-scale Large Language Model (LLM) from Mistral. The findings indicate that this approach achieves accurate information retrieval and provides reliable outputs, even when problem descriptions are vague.