Towards AI-enhanced process planning: assessing machine tool capability based on part design

The emergence of the fourth industrial revolution, or Industry 4.0, necessitates a more automated approach to manufacturing process planning. This process begins with evaluating machine tool capabilities to handle specific part geometries and microstructures. Once a match is established, the focus shifts to developing an efficient method for converting design elements into physical components. This work aims to create and validate a framework that assesses the manufacturability of design features based on the available machinery and materials. Specifically, it involves classifying manufacturing processes, such as turning and milling, for a given part design geometry. To achieve this, feature attributes like rotational symmetry and D2 distribution are calculated for a dataset used to train a decision tree. This model then suggests the appropriate manufacturing process for a given CAD model. The decision tree is validated with a separate dataset, showing reasonable accuracy. Ultimately, the goal is to enhance process planning, ensuring the seamless translation of designs into physical products, with a particular emphasis on geometry, microstructure, and cost.