AIML-Based Multi-Objective Optimization of Energy Use and Tool Life Under Financial Constraints

In modern manufacturing, it is important to maximize tool life in a way that saves energy and money. However, most current predictive maintenance models only optimize one goal at a time and do not have integrated frameworks that look at energy consumption, tool wear progression, and budgetary restrictions at the same time. This gap makes it harder to use AI-powered decision systems that may have an effect on the economy in the real world. This study was to create and test a multimodal, physics-based AI architecture that could optimize energy use, tool life, and operating cost at the same time under real-world machining circumstances. We conducted an experimental investigation utilizing several publicly accessible machining datasets that included sensor time-series, tool-surface photographs, process parameters, and energy logs. We used multi-objective measures, robustness tests, and studies of computing efficiency to compare a baseline (RF, LSTM–ResNet), a hybrid CNN–LSTM+GNN model, and the proposed DT-FTX Digital-Twin Foundation Transformer with physics-informed GNN and bilevel cost optimization. DT-FTX had the highest overall performance, with a 57% increase in hypervolume over the hybrid model and a more than 30% decrease in wear-prediction error and energy-forecast RMSE. It also showed the lowest degradation (8%) under unseen cutting conditions. It also had the best cost-per-cycle decrease, which shows that it modeled the trade-offs between energy, tool life, and cost well. These results suggest that DT-FTX is a far better approach to make decisions about machining that are based on economics. It also offers a strong and scalable base for the next generation of smart manufacturing systems.