Multimodal Artificial Intelligence Framework for Intelligent Inspection and Risk Assessment in Smart Manufacturing Environments
Keywords:
smart manufacturing, multimodal AI, intelligent inspection, risk assessment, large language models, edge intelligence, digital twin, system resilienceAbstract
The evolution of smart manufacturing systems toward fully autonomous and resilient operations hinges on the capacity to integrate heterogeneous data streams through advanced artificial intelligence frameworks. This paper presents a system-level investigation of a multimodal artificial intelligence architecture designed for intelligent inspection and continuous risk assessment within industrial environments. Rather than focusing on isolated algorithmic performance, the discussion interrogates the structural trade-offs inherent in fusing visual, acoustic, vibrational, and textual modalities, emphasizing the architectural design of fusion pipelines that preserve temporal alignment and semantic consistency across sensor networks. We analyze the growing role of foundation models and domain-specific large language models for reasoning over maintenance logs, anomaly descriptions, and operational procedures, drawing attention to the governance, robustness, and fairness challenges that emerge when these models are deployed in safety-critical manufacturing contexts. Through an interdisciplinary lens, the paper examines deployment topologies spanning edge, fog, and cloud layers, and evaluates the implications for latency, data sovereignty, and energy footprint. Cross-domain parallels with power generation infrastructure illustrate how inspection and risk frameworks must be instantiated under heterogeneous regulatory and operational constraints. Further, we explore policy considerations around liability, auditability, and human oversight when AI-based risk assessments inform production line shutdowns or adaptive process reconfiguration. The paper argues that the viability of multimodal inspection systems depends less on raw predictive accuracy and more on the engineered resilience of the entire socio-technical infrastructure, including mechanisms for uncertainty quantification, explainability, and accountable decision-making. We conclude with a forward-looking perspective on the convergence of digital twins, federated learning, and sustainable AI practices as enablers for trustworthy industrial intelligence.
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