Publicación:

Toward a digital ecosystem for additive manufacturing driven by standards-based digital thread and digital twins

Resumen

The ongoing digitalization of manufacturing is transforming how products are designed, produced, and optimized, driven by the convergence of the Digital Thread (DTh) and Digital Twin (DTw) paradigms. However, achieving seamless integration across these technologies remains a major challenge due to persistent issues of data interoperability, consistency, collaboration, and intelligent data exchange throughout the product lifecycle. Moreover, the limited intelligence at the machine level and the fragmentation of data across heterogeneous systems hinder the realization of fully connected, autonomous manufacturing environments. To address these challenges, this work conducts a comprehensive review of the literature on DTh and DTw technologies, focusing on their application in additive manufacturing (AM) and their alignment with international standards for data exchange and system interoperability. Building on this foundation, a unified digital ecosystem for contextualized intelligence is proposed, aiming to integrate DTh and DTw through standardized, semantically rich, and interoperable data flows. Furthermore, a standards-based DTh–DTw framework is presented, leveraging key industrial standards, including STEP/STEP-NC, MTConnect, QIF, OPC UA, MQTT, and ISO 23247, to ensure traceability, real-time synchronization, and data-driven decision-making across the AM lifecycle. Two implementation scenarios validate the proposed approach: (i) an FDM-based AM process using STEP-NC and MTConnect for integrated process planning and monitoring, and (ii) a robotic wire-based LMD cell featuring three DTw implementations compliant with ISO 23247 for real-time simulation, predictive maintenance, and process visualization. These implementations demonstrate the feasibility of constructing interoperable, data-centric manufacturing workflows using open standards. The results underscore the potential of the proposed ecosystem to enhance interoperability, data consistency, and intelligence across manufacturing processes, while also revealing current limitations in AM-specific standardization and cross-platform integration. Although focused on AM, the framework is inherently extensible to other manufacturing domains, paving the way toward standards-driven, intelligent, and generative manufacturing ecosystems.