Vision2DCS | Can AI Automate Engineering Schematics? скачать в хорошем качестве

Vision2DCS | Can AI Automate Engineering Schematics?

Welcome to the future of industrial automation design. In this video, I introduce Vision2DCS, a multimodal AI-driven pipeline designed to transform static engineering schematics (P&IDs) into dynamic automation configurations. As an Industrial AI Architect, I built Vision2DCS to bridge the gap between "dead" PDF data and modern Distributed Control Systems (DCS). By leveraging Computer Vision and Graph Neural Networks (GNN), this tool acts as an intelligent engineer, recognizing symbols, tracing connections, and drastically reducing manual engineering time. Key Technical Segments Discussed in This Video: The Problem: The inefficiency of manual data entry with its 1-5% error rate and the reality that engineers spend 30% of their time just searching for documentation. Intelligent Context: How Vision2DCS utilizes YOLOv11 and OCR technologies to ingest and parse symbols and technical tags (Tag IDs) from noisy industrial drawings,. Automated Synthesis: Moving from static line tracing to the deterministic creation of logical automation objects (Equipment and Control Modules) compliant with ISA-88 and ISA-95 standards,. Architecture Breakdown: A look under the hood at the Relationformer-based Image-to-Graph architecture and the automated code generation for Siemens PCS 7 and ABB 800xA,,. Support and Community: If you found this technical breakdown helpful, please like the video and subscribe to the channel for more content on industrial automation and AI. You can also join the Nexus Research Series on Patreon to support my work and access more in-depth technical articles, podcast episodes, and projects. Patreon:   / senanurcetin   Contact: Website: https://senanur-cetin.vercel.app/ #IndustrialAI #Vision2DCS #PID #YOLOv11 #GraphNeuralNetworks #DigitalTwin #nexus