The Digital Twin (DT) is gaining traction as a potential future cornerstone of additive manufacturing (AM) and Industry 4.0, despite its nascent stage and the vast scope of both technologies. DTs represent a physical system through real-time data exchange, offering simulation capabilities that address the inherent unpredictability of AM. This research specifically investigates the integration of DT in smaller AM shopfloors, aiming to understand the next steps towards its implementation. While larger companies with extensive resources spearhead DT development, this study reveals benefits, particularly for automation-focused smaller companies, even with potential initial investment challenges. Building a DT-aware business model can prepare smaller companies for the future as the technology matures. However, the research identifies cost-effective DT aspects that can offer substantial value and future-proof manufacturing processes for smaller shops. Further research will delve deeper into these aspects and their specific applications in smaller AM environments.

DTs improve simulation accuracy, reducing trial-and-error approaches and resource expenditure (e.g., time, material). However, integration with legacy machines presents a challenge, requiring further development. This research aims on identifying and predict the material usage, energy consumption, and machine efficiency as the primary areas for DT application. The investigation in materials processing using additive manufacturing will aim to evaluate the impact of DT integration on these parameters through empirical testing. The research aims to develop methods for seamless integration of legacy machines into the DT system. The ultimate aim is to explore the potential of DT for data-driven automation in smaller AM settings.

This research demonstrates the potential of DT technology to enhance resource management and efficiency in small-scale AM. Further investigation is needed to refine and validate the proposed DT solution and explore its broader applications in similar manufacturing environments.

Keywords: Digital Twin, Additive Manufacturing, Small-scale manufacturing, Resource management, Efficiency, Simulation, Automation