Neuro-mechanistic model for cutting force prediction in helical end milling of metal materials layered in multiple directions

Abstract

In machining of multi‐layer metal materials used frequently for the manufac‐ ture of transfer sheet‐metal forming tools, the cutting edge is often damaged because of cutting force peaks. Therefore, a neuro‐mechanistic model, pre‐ sented in this paper, has been created for accurate prediction of cutting forces in helical end milling of multidirectional layered materials. The generalized model created takes into account the complex geometry of the helical end milling cutter, the instantaneous chip thickness and the direction of deposit‐ ing of the individual layer of the multidirectional layered material considered in the calculation through predicted specific cutting forces. For the prediction of specific cutting forces for individual layers a neural network is incorpo‐ rated in the model. The comparison with experimental data shows that the model predicts accurately the flow of cutting force in milling of multidirec‐ tional layered metal materials for any combination of cutting parameters, tool engagement angle and directions of depositing three layers of material. The predicted cutting force values agree well with the values obtained, the maxi‐ mum error of predicted cutting forces is 16.1 % for all comparison tests per‐ formed. © 2020 CPE, University of Maribor. All rights reserved.