Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques

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. 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. Preprints 2024, 2024100099. 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.

Keywords

Additive Friction Stir Deposition; Artificial Intelligence; Multimodal; RAG; Additive Manufacturing

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.