A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG Approaches

Ryan Singh; Austin Hamilton; Amanda White; Michael Wise; Ibrahim Yousif; Arthur Carvalho; Zhe Shan; Reza Abrisham Baf; Mohammad Mayyas; Lora A. Cavuoto; Fadel M. Megahed

arXiv · 2511.11847

A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG Approaches

Ryan Singh·Austin Hamilton·Amanda White·Michael Wise·Ibrahim Yousif·Arthur Carvalho·Zhe Shan·Reza Abrisham Baf·Mohammad Mayyas·Lora A. Cavuoto·Fadel M. Megahed

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Abstract

Ensuring worker safety remains a critical challenge in modern manufacturing environments. Industry 5.0 reorients the prevailing manufacturing paradigm toward more human-centric operations. Using a design science research methodology, we identify three essential requirements for next-generation safety training systems: high accuracy, low latency, and low cost. We introduce a multimodal chatbot powered by large language models that meets these design requirements. The chatbot uses retrieval-augmented generation to ground its responses in curated regulatory and technical documentation. To evaluate our solution, we developed a domain-specific benchmark of expert-validated question and answer pairs for three representative machines: a Bridgeport manual mill, a Haas TL-1 CNC lathe, and a Universal Robots UR5e collaborative robot. We tested 24 RAG configurations using a full-factorial design and assessed them with automated evaluations of correctness, latency, and cost. Our top 2 configurations were then evaluated by ten industry experts and academic researchers. Our results show that retrieval strategy and model configuration have a significant impact on performance. The top configuration, selected for chatbot deployment, achieved an accuracy of 86.66%, an average cost of $0.005 per query, and an average end-to-end latency of 10.04 seconds. This latency is practical for delivering a complete safety instruction and is measured from query submission to full instruction delivery rather than generation onset. Overall, our work provides three contributions: an open-source, domain-grounded safety training chatbot; a validated benchmark for evaluating AI-assisted safety instruction; and a systematic methodology for designing and assessing AI-enabled instructional and immersive safety training systems for Industry 5.0 environments.

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Ryan Singh, Austin Hamilton, Amanda White, Michael Wise, Ibrahim Yousif, Arthur Carvalho, Zhe Shan, Reza Abrisham Baf, Mohammad Mayyas, Lora A. Cavuoto, Fadel M. Megahed. 2026-02-10. A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG Approaches. https://arxiv.org/abs/2511.11847

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