CNC machining data repository: Geometry, NC code & high-frequency energy consumption data for aluminum and plastic machining

Data is often referred to as the ``oil of the future,'' playing a crucial role in various applications, including the training of machine learning models. In the field of manufacturing, high-quality datasets are essential for optimizing production processes, improving energy efficiency, and developing predictive maintenance strategies. This paper introduces a comprehensive CNC machining data repository that includes three key data categories: (1) product geometry data, (2) NC code data, and (3) high-frequency energy consumption data. This dataset is particularly valuable for researchers and engineers working in manufacturing analytics, energy-efficient machining, and machine learning applications in smart manufacturing. Potential use cases include optimizing machining parameters for energy reduction based on power consumption patterns, and enhancing digital twin models with real-world machining data. By making this dataset publicly available, we aim to support the development of data-driven solutions in modern manufacturing and facilitate benchmarking efforts across different machining strategies.