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<p>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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {ManuRAG}_4\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mtext>ManuRAG</mtext> <mn>4</mn> </msub> </math></EquationSource> </InlineEquation> 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.</p>

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ManuRAG: multi-modal retrieval augmented generation for manufacturing question answering

  • 期刊论文

  • 发表日期：

    2026年3月15日

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  • Yunqing Li,
  • Zihan Dong,
  • Farhad Ameri,
  • Jianbang Zhang

摘要

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 \(\text {ManuRAG}_4\) ManuRAG 4 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.