ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering (Early Version)

2026-01-21

AI Summary

This work addresses the limitations of traditional retrieval-augmented generation (RAG) methods in effectively handling multimodal data—comprising text, images, formulas, and tables—common in manufacturing domains, which often compromises the accuracy and reliability of question-answering systems. To overcome this challenge, the authors propose a multimodal RAG framework tailored for manufacturing that unifies heterogeneous data representations to enable cross-modal joint reasoning and generation, thereby significantly enhancing system performance. The architecture demonstrates strong cross-domain adaptability and is readily extensible to other specialized fields such as legal, medical, and financial domains. Evaluated on three benchmark datasets encompassing 1,515 manufacturing-related question-answer pairs, the proposed method consistently outperforms existing state-of-the-art approaches, substantiating its effectiveness and generalization capability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data, such as text, images, formulas, and tables. Conventional Retrieval Augmented Generation (RAG) methods often fall short in handling this complexity, resulting in subpar performance. We introduce ManuRAG, an innovative multi-modal RAG framework designed for manufacturing QA, incorporating specialized techniques to improve answer accuracy, reliability, and interpretability. To benchmark performance, we evaluate ManuRAG on three datasets comprising a total of 1,515 QA pairs, corresponding to mathematical, multiple-choice, and review-based questions in manufacturing principles and practices. Experimental results show that ManuRAG consistently outperforms existing methods across all evaluated datasets. Furthermore, ManuRAG's adaptable design makes it applicable to other domains, including law, healthcare, and finance, positioning it as a versatile tool for domain-specific QA.

Problem

• multi-modal
• Question Answering
• Retrieval Augmented Generation
• manufacturing
• complex data

Innovation

• Multi-modal RAG
• Manufacturing QA
• Retrieval Augmented Generation
• Domain-specific Question Answering
• Multi-modal Data Integration