Data Collection Framework Enabling Simulation-Based Development and Evaluation of AM Post-Processing

Abstract

In recent years, Additive Manufacturing (AM) has expanded its commercial potential outside rapid prototyping use cases. AM has become a noteworthy option when manufacturing methods for components are selected. In many cases, flexibility of AM production overcomes the drawbacks it holds compared to traditional manufacturing methods. During this evolution, a vast amount of research about AM technologies has been carried out. However, only a few researchers have focused on manufacturing paradigms of industrial scale AM plants. One reason for this is the complexity and variety of AM post processing tasks. Further, in powder-based processes safety issues can become a barrier for building and utilizing a full-scale research environment. Despite the forementioned challenges, it is essential to carry out holistic research on AM processes to assure it meets the requirements of the smart and green manufacturing era. In this paper we propose a data collection framework that enables simulation-based development and evaluation of AM post processing. This framework covers a data pipeline from Matlab-Simulink based process simulation environment up to FIWARE IoT platform. It enables data generation and collection from simulated AM post processing workflows, opening several new research paths for automation concept development, sustainability analysis, synthetic data generation and Digital Twin development.