Predictive maintenance for industrial equipment is critical for improving production safety, reducing maintenance costs, and optimizing equipment utilization. However, existing deep learning methods face two key challenges in industrial equipment prognostics: the lack of uncertainty quantification to support risk-informed decision-making and the inability to simultaneously capture multi-scale temporal patterns in equipment degradation processes. This paper presents the Temporal Probabilistic Joint Embedding Predictive Architecture (TP-JEPA), a novel deep neural network framework that learns robust representations of equipment health states by predicting probabilistic distributions of future states in latent space. TP-JEPA’s innovations include: (1) a probabilistic encoding mechanism that extends deterministic representations to distributions, inherently quantifying prediction uncertainty; (2) a multi-scale temporal encoder designed to extract hierarchical features from high-frequency transients to long-term degradation trends; and (3) a multi-task learning paradigm that jointly optimizes anomaly detection, remaining useful life (RUL) estimation, and health state assessment, enabling synergistic task enhancements. Evaluations on the National Aeronautics and Space Administration (NASA) bearing dataset demonstrate that TP-JEPA achieves an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.9999 for anomaly detection—outperforming state-of-the-art methods—and a mean absolute error of 69.1 cycles for remaining useful life prediction, with well-calibrated uncertainty estimates (95% confidence interval coverage of 94.6%). Cross-dataset validation and ablation studies confirm the framework’s efficacy and robustness.

Metallic lattice structures have emerged as revolutionary lightweight materials with exceptional specific strength and multifunctional capabilities, particularly in aerospace applications. This review comprehensively examines the state of the art in laser additive manufacturing (LAM) of metallic lattice structures, focusing on selective laser melting (SLM) and electron beam melting (EBM). We critically analyze recent advances in materials development, including nickel-based superalloys (GH4169/IN718, K465), titanium alloys (Ti-6Al-4V), and functionally graded composites. The review addresses key design considerations, including unit cell topology optimization, node reinforcement strategies, and gradient structures. We examine mechanical properties under various loading conditions, thermal management capabilities, and failure mechanisms through both experimental and numerical perspectives. Advanced detection methods, including micro-CT imaging and AI-based defect identification, are evaluated for quality assurance. Critical challenges, including surface roughness control, residual stress management, and size limitations, are discussed alongside emerging opportunities in machine-learning-assisted design and multi-material systems. This review provides essential insights for researchers and engineers seeking to advance lattice-structured applications in next-generation aerospace systems.

System of Systems Engineering (SoSE) is an emerging discipline focused on the design, integration, and evolution of metasystems—composed of multiple autonomous yet interdependent systems. These systems of systems (SoS) offer capabilities that exceed those of any individual constituent systems by enabling synergistic coordination, interoperability, and dynamic adaptation across diverse operational contexts. This study pursued three primary objectives: (i) to examine the role of integration in enabling emergent capabilities beyond those of individual systems, (ii) to explore SoSE as a framework for orchestrating Future Factory-to-Factory (F2F) Manufacturing, and (iii) to assess the implications of SoSE to address volatile, uncertain, complex, and ambiguous (VUCA) manufacturing environments. Data for this study were drawn from the Web of Science Core Collection—a globally recognized citation database that informs research evaluation and planning across more than 7000 institutions. Our analysis revealed that SoSE is gaining importance as a unifying paradigm for managing enhanced capability of complex, distributed systems characterized by operational and managerial independence, evolutionary development, and emergent behaviors. Findings indicated persistent challenges across technical, operational, economic, governance, and strategic domains, underscoring the need for holistic integration strategies. In the context of Future F2F Manufacturing, SoSE holds a significant promise for enhancing system-wide coordination, resilience, and adaptability. The study concludes by emphasizing the importance of embedding Systems Theory principles to guide the sustainable and scalable integration of interconnected manufacturing ecosystems to maintain viability.