Abstract

Efficient cutting tool selection and precise cost estimation play a vital role in modern manufacturing, significantly influencing productivity, energy efficiency, and overall process sustainability. This study proposes an artificial intelligence driven framework for predicting cutting tool costs while optimizing machining parameters to reduce processing time and material waste. The methodology integrates key machining performance indicators, including tool wear, energy consumption, and surface quality, with advanced machine learning models such as Random Forest, XGBoost, and Decision Trees. To enhance model transparency and interpretability, explainable AI techniques, particularly SHAP, are utilized to analyze predictions and identify the most influential factors affecting tool cost and performance. The results demonstrate that the proposed framework not only achieves high accuracy in cost prediction but also provides actionable insights for optimizing machining conditions, extending tool life, and lowering operational costs. Overall, the study highlights the effectiveness of combining data driven predictive modeling with sustainable manufacturing principles to enable cost efficient, high performance, and environmentally responsible machining operations.

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