International Journal of Scientific Research and Engineering Development

Smart manufacturing environments have witnessed rapid growth in automation and condition monitoring, yet real-time troubleshooting guidance for factory-floor personnel continues to lag behind these advances. Technical documentation for industrial equipment is predominantly available in English and employs domain-specific vocabulary that proves difficult for operators to navigate swiftly under fault conditions. This work presents a multilingual, voice-capable AI assistant that supports equipment troubleshooting by extracting knowledge from unstructured machine documentation. The system integrates Natural Language Processing with dense vector retrieval to surface contextually relevant passages from large technical corpora. Both spoken and typed operator queries are handled, semantically compared against indexed document chunks, and translated into actionable repair guidance. The assistant lowers the barrier to technical knowledge and helps maintenance personnel resolve equipment faults with greater speed and confidence. Key engineering challenges addressed include crosslingual communication in noisy shop-floor conditions and knowledge extraction from poorly structured documentation. Evaluation outcomes suggest that dialogue-based AI can meaningfully reduce reliance on specialist technicians, shorten equipment downtime, and raise overall plant productivity. The study underscores the importance of human-centered intelligent systems that combine voice interaction, cross-lingual support, and semantic document retrieval within Industry 4.0 manufacturing workflows.

Dnyanesh Badave, Shivani Budhkar, Abhishek Sharma, Shubham Gugale, Gaurav Patil,"Multilingual Voice-Enabled AI Chatbots for Industrial Troubleshooting Using Unstructured Machine Manuals" International Journal of Scientific Research and Engineering Development, V9(3): Page(1141-1145) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.