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Integrating digital twin approaches with physics-informed machine learning is necessary to overcome the computational and data limitations of traditional models, enabling real-time thermal control and adaptive optimization in FFF/FDM additive manufacturing. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Systematic review of multiscale thermal prediction models for FDM/FFF additive manufacturing: Toward digital twin integration with physics-informed machine learning

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