@masterthesis{Sattler, type = {Bachelor Thesis}, author = {Sattler, Martin}, title = {KI-gest{\"u}tzte Optimierung der Werkzeugstandzeit in CNC-Maschinen}, doi = {10.60524/opus-2821}, pages = {117}, abstract = {This thesis presents the development of a prototype, locally deployable AI system for predicting tool life in CNC machines. Its goal is to optimize maintenance cycles and reduce downtime through data-driven analysis. The system uses machine learning methods implemented with TensorFlow/Keras and follows a modular architecture separating the data pipeline, model, and interface integration. Historical machine data are preprocessed, encoded, and scaled for training. The evaluation shows that the prototype is technically functional but fails to meet its predictive goal, reaching an error rate of about 80\%. The main reasons are the limited dataset and inconsistent input quality. This underlines the strong dependency of model performance on data completeness and consistency. The conclusion identifies key improvements: expanding data sources, refining feature engineering, hyperparameter tuning, and integrating real-time production data. The prototype thus provides a foundation for future research toward higher predictive accuracy and industrial applicability.}, language = {de} }