Production systems of the automotive industry process parts that were previously designed and manufactured according to different manufacturing technologies. In car body architectures, additive manufacturing (AM) has become a relevant technology for supplementing conventional manufacturing technologies, e.g., casting or forming technologies. This paper presents a methodology for an automatic and objective early-stage analysis of part features and the subsequent identification of the parts’ most suitable manufacturing technology. For this purpose, a comprehensive database is required, in which several technological and economic parameters need to be derived and predicted, including part requirements, production inherences, expected lifecycle costs, as well as geometric information. Based on this, data screening allows to effectively evaluate the technological and economic potential for a component to be manufactured either conventionally or additively in early product development phases. One core element is the part requirements derivation and analysis within one novel module of the part screening methodology. Subsequently, the product development process and the production system can be adapted according to the identified, most promising manufacturing technologies. Hence, this early-stage decision allows for cost reduction through an increased planning reliability. This work thus contributes to a successful co-evolution of smart product development and the production processes.

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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.