Prediction of breakthrough extruding force in large-scale extrusion process using artificial neural networks

Accurate prediction of breakthrough extruding force is very important for extrusion production, especially for the large-scale extrusion process, which directly affects the production costs and safety. In this paper, based on the production data of the 360-million-newton-tonnage extruding machine, an artificial neural network (ANN) algorithm is used to establish the breakthrough extruding force prediction model for the large-scale extrusion process, and the calculation results are validated. Results show that the proposed model has high accuracy, and the average relative error between the predicted and experimental values is only 1.79%. Further, problems that are difficult to quantitative analyze such as die wear and glass powder residue in actual production, which can be regarded as "noises," are studied. Finally, the model presented is compared with the traditional finite element (FE) model. The accuracy of the ANN model is 10.2 times that of the FE model. Thus, the model established in the study fully considers the difference between actual production and theoretical analysis and provides an effective method for accurately predicting the breakthrough extruding force.