Additive Manufacturing 2.0 for Metals: State of the Art in Process Signatures, In-Situ Monitoring, and Qualification
Keywords:
Additive Manufacturing 2.0, Metal 3D Printing, Process Signatures, In-Situ Monitoring, Machine Learning, Qualification and Certification, Digital Twin, SustainabilityAbstract
This study aimed to synthesize and critically evaluate recent advances in Additive Manufacturing 2.0 (AM 2.0) for metals, focusing on the integration of process signatures, in-situ monitoring systems, and qualification frameworks as enablers of intelligent, first-time-right production. A qualitative systematic review design was employed to explore conceptual and technological developments across 13 peer-reviewed journal articles published between 2018 and 2025. These sources were purposively selected from major scientific databases, including Scopus, Web of Science, and ScienceDirect, based on their relevance to process control, in-situ sensing, and certification in metal additive manufacturing. Data were analyzed through a multi-stage thematic content analysis using NVivo 14 software. Open, axial, and selective coding were applied until theoretical saturation was achieved, resulting in the identification of four overarching themes: (1) process signatures and metallurgical characteristics, (2) in-situ monitoring and data-driven control, (3) qualification and standardization frameworks, and (4) future challenges and research directions in AM 2.0. Results revealed that process signatures serve as both diagnostic and predictive indicators of melt-pool behavior, defect formation, and microstructural evolution. In-situ monitoring technologies are evolving from single-sensor systems toward multi-sensor, machine-learning-driven architectures capable of real-time defect detection and adaptive control. However, industrial implementation remains constrained by sensor calibration issues, data interoperability, and latency in feedback mechanisms. The review also found that qualification and certification strategies are progressing through the adoption of advanced non-destructive evaluation (NDE) methods and digital traceability frameworks, yet global standardization gaps persist. Future research should emphasize automation, sustainability, human–machine collaboration, and the integration of AI-based predictive control. AM 2.0 represents a paradigm shift toward intelligent, cyber-physical manufacturing ecosystems where sensing, analytics, and certification co-evolve. Achieving industrial scalability will require harmonizing process intelligence with standardized qualification and sustainable practices.
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References
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