Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures.

Non-insulated heat exchangers in gas-to-gas service lose substantial energy to the surroundings. This study evaluates Al₂O₃ and ZnO ceramic coatings (200 $\mathsf{\mu }$m) as passive thermal retention layers on the inner surface of the outer tube in a copper double-pipe heat exchanger, using 3D CFD simulations verified for internal consistency against Log Mean Heat Transfer Rate analytical solutions. Six cases were modelled: three coating conditions across parallel-flow and counter-flow configurations under laminar conditions ($R{e}_i\approx 525$, $R{e}_o\approx 192$) with air as the working fluid. The coating elevates the outer tube inner wall temperature $T_3$, increasing the convective driving force to the cold fluid while suppressing ambient dissipation. In parallel flow, Al₂O₃ increases the net inter-fluid heat transfer rate by 35.7% and reduces ambient losses by 81.4%; ZnO achieves 30.9% and 70.4%, respectively. In counter-flow, Al₂O₃ yields a 26.6% enhancement and 73.2% loss reduction. The coated parallel-flow configuration outperforms the uncoated counter-flow baseline. Thermal circuit analysis shows that Al₂O₃ superiority arises from its higher conductivity (40 vs. 19 W m⁻¹ K⁻¹), which sustains a higher equilibrium $T_3$ and a heat partition ratio of 11.84 versus 7.17 for ZnO. These results show that a single ceramic coating layer can recover a large fraction of the thermal energy lost through non-insulated walls, offering a low-cost, retrofit-compatible pathway to improve the energy efficiency of gas-to-gas heat exchangers in HVAC, building energy recovery, and industrial process heat applications.

Power-to-Ammonia-to-Power (P2A2P) systems in hot-arid MENA climates reject waste heat through Recuperated Organic Rankine Cycles (RORCs) whose condensation temperature tracks ambient conditions across annual swings exceeding 35 K. Standard exergoenvironmental assessments evaluate priorities at a single dead-state temperature, an assumption this study terms the Static $T_0$ Fallacy. A four-way advanced exergy decomposition is combined with an off-design model (Stodola’s ellipse, constant-$UA$ scaling) to sweep the dead-state temperature from ${5 }^{\circ }$C to ${40 }^{\circ }$C. At $T_0={20 }^{\circ }$C, 95.8% of total exergy destruction is endogenous, confirming weak inter-component coupling. The condenser carries the largest avoidable environmental impact rate (4.02 mPts/h, 74% of the system total). At $T_0={37.4 }^{\circ }$C, the recuperator undergoes a priority inversion from destruction-dominated ($f_b=14.7\%$) to fully capital-dominated ($f_b=100\%$). Ammonia’s wet-fluid thermodynamic coupling eliminates the recuperator’s duty as the condensation temperature approaches the cold-side outlet constraint. The recuperator’s avoidable environmental impact rate drops by 100% relative to the standard assessment, while the condenser’s rises by 49%. This inversion mechanism is fluid-specific: the ammonia recuperator’s endogenous fraction reaches 99.5% at $T_0={20 }^{\circ }$C. The toluene recuperator, swept at its own independently optimised operating point, has an endogenous fraction between 59.08% and 82.31% over the same range. For P2A2P installations where the annual ambient swing exceeds 15 K, exergoenvironmental analysis should be performed at both design-season and summer-peak dead-state temperatures, with the summer-peak result governing capital allocation.