Optimization of Temperature Control in Extrusion Machines Using Machine Learning Algorithms

Extrusion machines require precise temperature control to maximize energy savings during manufacturing processes and ensure product quality. This paper presents a new Machine Learning (ML) based strategy for optimizing temperature regulation in real-time. The suggested method learns the behavior patterns of extrusion machines under various operating settings and then dynamically adjusts the stress and temperature parameters to achieve faster and more accurate management. The models and experiments demonstrate a notable reduction in temperature fluctuations and a notable improvement in energy consumption when compared to traditional control methods. Additionally, by employing machine learning, any irregularities in the process may be anticipated, enhancing the system's long-term stability and functionality. This method offers a flexible and effective way to regulate temperature in industrial settings, which might revolutionize extrusion operations.