Modeling and Precise Prediction of Unobservable Pouring Processes Based on Machine Learning - Yang, Shengluo; Yin, Shuoxin; Xu, Zhigang (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
Accurate online measurement of pouring speed and charge amount remains a critical challenge in complex component manufacturing. This challenge is primarily due to the retention and adhesion of molten material in pipelines and cavities, preventing direct measurement of outlet flow. As a result, process quality heavily relies on operator experience, leading to inconsistent results and frequent defects. To address this, we propose a machine learning (ML)-based soft-sensing approach combined with multi-sensor data fusion. By integrating process parameters—such as material viscosity, temperature, and vacuum level—with structural characteristics of the pouring system, we develop a nonlinear mapping model that transforms unmeasurable outlet parameters into a regression task. Six algorithms were evaluated under identical conditions, with three showing superior accuracy in capturing both global trends and local fluctuations. This framework enables high-precision, real-time prediction of pouring parameters, laying the foundation for intelligent pouring and improved process control.