首页|New Findings in Machine Learning Described from Faculty of Metals Engineering an d Industrial Computer Science (Optimizing Continuous Casting through Cyber-Physi cal System)
New Findings in Machine Learning Described from Faculty of Metals Engineering an d Industrial Computer Science (Optimizing Continuous Casting through Cyber-Physi cal System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting from the Faculty of Metals Engineering and Industrial Computer Science by NewsRx journalists, resear ch stated, "This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling." Funders for this research include National Centre For Research And Development: Intelligent Development Operational Program. Our news correspondents obtained a quote from the research from Faculty of Metal s Engineering and Industrial Computer Science: "The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders fo r the melt shop in the form of melt sequences, manages technological knowledge r egarding the principles of sequencing, and utilizes machine learning and optimiz ation methods in melt sequencing. Subsequently, production according to the impl emented plan is monitored using IoT and vision tracking systems for ladle tracki ng. During monitoring, predictions of energy demand and energy consumption in LM S processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: op timization of the heat sequence and at the production level through the predicti on of heating time. Optimization models and machine learning tools, including ma inly neural networks, are utilized. The system described includes key components : optimization models for sequencing heats using Ant Colony Optimization (ACO) a lgorithms and neural network-based prediction models for power-on time."
Faculty of Metals Engineering and Indust rial Computer ScienceCyborgsEmerging TechnologiesMachine Learning
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