AUTOMATED DESIGN SYSTEM FOR A BALL-END MILLING CUTTER WITH VISUALIZATION AND CLUSTERING OF PARAMETERS

The rapid evolution of technology necessitates the integration of automated information systems into a unified digital environment to boost the quality and competitiveness of high-tech products and complex technical systems. Mechanical engineering enterprises, as crucial players in the industrial sector, must establish a single information space that connects production, design, technological development, labor organization, management, and research. Recent advances in artificial intelligence further extend the capabilities of automated systems. AI not only complements existing tools but also transforms design, management, and operational approaches by making systems more adaptive and intelligent. Python supports key AI applications such as machine learning, neural networks, natural language processing, and computer vision. Libraries like Matplotlib, Pandas, and Pyplot provide powerful tools for data analysis and visualization, allowing researchers to generate clear and informative graphs. The interactive environment of Jupyter Notebook further enhances the data analysis workflow. Machine learning techniques, particularly clustering via Scikit-learn’s K-means algorithm, help classify tools based on key parameters, optimizing their design and manufacturing processes. Visual data representation simplifies analysis and aids in detecting hidden dependencies within the dataset. The integration of modern visualization methods and machine learning techniques into an automated tool design system enhances project efficiency and production process optimization. The implementation of such advanced software accelerates design workflows, improves calculation accuracy, and simplifies 3D modeling.