Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing

Metal-based Additive Manufacturing (AM) can realize fully dense metallic components and thus offers an opportunity to compete with conventional manufacturing based on the unique merits possible through layer-by-layer processing. Unsurprisingly, Machine Learning (ML) applications in AM technologies have been increasingly growing in the past several years. The trend is driven by the ability of data-driven techniques to support a range of AM concerns, including in-process monitoring and predictions. However, despite numerous ML applications being reported for different AM concerns, no framework exists to systematically manage these ML models for AM operations in the industry. Moreover, no guidance exists on fundamental requirements to realize such a cross-disciplinary platform. Working with experts in ML and AM, this work identifies the fundamental requirements to realize a Machine Learning Operations (MLOps) platform to support process-based ML models for industrial metal AM (MAM). Project-level activities are identified in terms of functional roles, processes, systems, operations, and interfaces. These components are discussed in detail and are linked with their respective requirements. In this regard, peer-reviewed references to identified requirements are made available. The re-quirements identified can help guide small and medium enterprises looking to implement ML solutions for AM in the industry. Challenges and opportunities for such a system are highlighted. The system can be expanded to include other lifecycle phases of metallic and non-metallic AM.

Automated summary

Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.