| Abstract Scope |
This paper aims to conduct a comprehensive and in-depth study of the "AI Online Sorting System for Anode Guide Rod Groups in the Anode Assembly Workshop" for use in the aluminum electrolysis industry. Integrating the latest research findings and industry practices in artificial intelligence, machine vision, and automation, this paper systematically explains the project background, technical solutions, performance indicators, and industrial applications of the system, and provides a detailed analysis of the AI algorithm architecture and software functions. By integrating high-precision industrial vision, deep learning algorithms, and automated control technologies, this system effectively replaces traditional manual inspection, achieving rapid, accurate, and automated identification and sorting of anode guide rod group defects, significantly improving product quality and production efficiency while reducing safety hazards. Furthermore, by establishing a database of data on the entire lifecycle of anode guide rods, it provides strong data support for the digital transformation and refined management of aluminum plants.
Keywords: anode rod assembly; online identification; artificial intelligence; industrial vision; deep learning; real-time performance; |