Digital Twin-Driven Approach to Predict Cutting Forces and Temperature in Turning of Ti 6246 Alloy

A digital twin-driven approach is used in turning of Ti 6246 alloy for forecasting cutting forces and temperature based on finite element (FE) analysis, fuzzy inference system and experimental data. An FE model was established for the turning of the studied alloys to study the temperature distributions, cutting forces, and stresses under different cutting conditions. The FE model was validated through experimental validation at selected cutting conditions. Additionally, a fuzzy inference logic-based model was developed based on the findings of the FE model for a range of selected cutting conditions. The results obtained through FE model are used as input to the fuzzy model to build a predictive framework for temperature and cutting forces for a variety of cutting parameters. The unpredictability exists in turning operation for the prediction of machining selected parameters is accounted in the Fuzzy logic model due to its magnetism to manage complicated and uncertain data. The suggested digital twin approach contributes to expands turning operation understanding in industries whereas also improves in perceptive recommendations for the optimization of machining parameters. Moreover, the proposed approach will improve overall machining effectiveness in aerospace and materials engineering applications by making the process optimization easier and help to create more dependencies as well as enhance effective production strategies.