Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Abstract

The selection and interaction of various manufacturing technologies are key difficulties in product development and production processes. A component’s geometry is one of the most important factors to consider when choosing the best technology. This article presents a method for an automated geometry analysis of metallic components. The goal is to analyze manufacturing technology alternatives regarding their capability to create required geometries. It also aims at short computing times since the outcome of this geometric analysis supplements a part screening methodology for the selection of the most suitable manufacturing technology for each component. To achieve a successful classification, artificial intelligence (AI) approaches are trained with images of the components that are labeled with suitable manufacturing technologies. The AI models hence learn how components of different manufacturing technologies look like and which characteristics they embody. To support the classification model, object recognition models are tested to automatically extract component features such as holes, coinages, or profile compositions. After training and comparing different AI approaches, the best performers are selected and implemented to analyze unseen image data of upcoming projects. In summary, this article’s research unifies existing AI approaches for image analyses with the field of production technology and product development. It provides a general methodology for applying image classification and object detection approaches in development processes of metallic components.