Predicting the deformation force in the incremental sheet forming of AA3003

Abstract The incremental sheet forming (ISF) is a new technique in which the metal sheets of low thickness are shaping with the help of a simple tool. The ISF is useful for rapid prototyping, customized product, or low volume product. ISF requires a simple fixture for griping the thin metal sheets. The present study included the experiments and the ANN model development for the prediction of deformation force in ISF of AA3003 alloy. The input variables are taken as steps-downsize, the feed rate of the tool, RPM of the spindle, wall angle, metal thickness, and density of lubricant. The recorded deformation force is in the range of 3.021–89.904 N). The minimum deformation force is noticed as compared to the deformation of 0.2 mm thick metal sheets when ISF is conducted with low level of step-downsize i.e. 0.1 mm. The variation in the observed deformation force is predicted with the help of artificial intelligence (AI). Subsequently, artificial neural network (ANN) is being utilized for the prediction of the deformation force. The results show that the overall coefficient of regression is 92.569% and the mean error (MAE) of 5.878.