Version 1
: Received: 1 October 2024 / Approved: 1 October 2024 / Online: 1 October 2024 (16:59:09 CEST)
How to cite:
Mishra, A. AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques. Preprints2024, 2024100099. https://doi.org/10.20944/preprints202410.0099.v1
Mishra, A. AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques. Preprints 2024, 2024100099. https://doi.org/10.20944/preprints202410.0099.v1
Mishra, A. AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques. Preprints2024, 2024100099. https://doi.org/10.20944/preprints202410.0099.v1
APA Style
Mishra, A. (2024). AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques. Preprints. https://doi.org/10.20944/preprints202410.0099.v1
Chicago/Turabian Style
Mishra, A. 2024 "AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques" Preprints. https://doi.org/10.20944/preprints202410.0099.v1
Abstract
A novel approach to enhance Additive Friction Stir Deposition (AFSD) is presented through the integration of Multimodal Retrieval Augmented Generation (RAG). AFSD which is a solid-state metal additive manufacturing process characterized by its complexity, necessitating advanced analytical tools. A multimodal RAG system is implemented, integrating textual, and visual data. The methodology involves text extraction using LangChain's PyPDFLoader, followed by chunking and embedding generation via Google's Generative AI model. ChromaDB is utilized for vector storage and efficient information retrieval. The system, powered by Google's Gemini large language model, demonstrates proficiency in generating detailed explanations of friction-based deposition processes, identifying manufacturing techniques from visual data, and evaluating material microstructures. Information from multiple sources is synthesized to produce context-aware responses. Process control, quality assurance, and decision-making in AFSD are significantly enhanced by this approach. The system's capabilities are illustrated through the analysis of various friction-based deposition techniques and microstructure evaluation.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
