A Methodology and Replication Roadmap for Designing Digital Twins in Additive Manufacturing

The industrial adoption of Digital Twins (DTs) for optimizing Additive Manufacturing (AM) processes is frequently hindered by the lack of standardized, reproducible frameworks. This report addresses this gap by proposing a structured methodology for the design and deployment of an end-to-end Digital Twin (DT) architecture for Laser Powder Bed Fusion (LPBF) processes. The core of this methodology is the synergistic fusion of two components: a physics-based simulation model based on Finite Element Model (FEM) that captures the complex physics phenomena of the process, and an AI-powered model for in-situ visual defect detection during the printing process. The entire system is orchestrated by a microservices backbone utilizing containerization and a modular big-data architecture that is realized via an event-driven message broker that ensures seamless data communication and interoperability. The aim of this work is to provide a clear, actionable guide and a generalized replication roadmap as a blueprint for researchers and engineers to systematically develop and deploy sophisticated DT systems, thereby bridging the gap between theoretical concepts and industrial viability.