The repository administrator has not yet configured an RDF license.
Modeling of Cutting Forces When End Milling of Ti6Al4V Using Adaptive Neuro-Fuzzy Inference System
The forces that are acting on a tool throughout the process of metal cutting are extremely important. An understanding of these forces can help to estimate the power required when cutting, as well as helping to ensure the subsequent structures are free from vibration and are adequately rigid. Several techniques have been utilised to model the responses throughout the metal cutting process. Adopting soft computing approaches has driven the artificial intelligence to solve complex, non-linear problems in different fields, such as modelling and predicting problems in metal cutting. In this study, an adaptive neuro-fuzzy inference system (ANFIS) has been used for predicting the cutting forces during dry end milling with uncoated insert of a Ti6Al4V alloy. The developed ANFIS model has been trained and tested with real experimental case study. Membership function with generalized bell shape was used with numbers 2, 3, 4, and 5. The absolute percentage error (MAPE) was calculated for models to pick up the minimum one. Good matching was obtained between experimental and ANFIS results. © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.
Springer Science and Business Media Deutschland GmbH
Studies in Systems, Decision and Control
HTML Summary of #20579 Modeling of Cutting Forces When End Milling of Ti6Al4V Using Adaptive Neuro-Fuzzy Inference System