Search Results (254)

Composite materials have traditionally been employed in the aerospace sector due to their ability to withstand highly demanding service conditions. In recent years, their application has expanded significantly into other engineering domains, including wind energy, shipbuilding, and the automotive industry. The design of composite structures often involves geometric discontinuities, such as cut-outs for access or fastener holes for mechanical joining, which typically become critical regions under load. Consequently, the stress concentrations induced by notches represent a major concern, as they can lead to substantial reductions in strength compared with unnotched laminates. A comprehensive understanding of the behaviour of notched specimens is therefore essential for the design of complex composite assemblies, where components are commonly joined using bolts and rivets. The objective of this study is to examine the tensile response and notch sensitivity of carbon fibre-reinforced polymer (CFRP) laminates with different stacking sequences, through a comparative analysis of unnotched and open-hole specimens. A central circular hole was introduced to reproduce the geometric discontinuities frequently encountered in structural applications, enabling a detailed assessment of stress concentration effects. The experimental results indicate that unidirectional laminates exhibit the highest sensitivity to notches, whereas quasi-isotropic configurations among the multidirectional laminates display the most pronounced reduction in strength, approaching 50%. Moreover, the Point Stress Criterion (PSC) and the Average Stress Criterion (ASC) were employed to determine the characteristic lengths of the specimens, revealing significant differences among the values obtained for each lay-up configuration. Overall, the findings highlight the strong influence of stacking sequence on the mechanical response of notched CFRP laminates and underscore the need to further refine existing failure criteria to accommodate novel laminate architectures, including Bouligand-type helicoidal bioinspired stacking sequences. Full article

Laser Powder Bed Fusion (LPBF) enables the fabrication of complex titanium alloy components with high geometric freedom; however, surface integrity and tribological performance remain critical limitations for sliding-contact applications in biomedical and aerospace systems. In this study, the influence of in-layer laser remelting on the microstructure, surface topography, and dry sliding tribological behavior of LPBF-fabricated Ti-6Al-4V ELI (Grade 23) is systematically investigated. Disc-shaped specimens were produced using single-scan (SS) and double-scan (DS, in-layer remelting) strategies and tested in ball-on-disc configuration against AISI 52100 steel at a constant normal load of 10 N and three sliding speeds of 0.10, 0.15, and 0.20 m·s⁻¹. Microstructural and phase-related characteristics were analyzed by X-ray diffraction combined with Rietveld refinement and Warren–Averbach analysis, revealing that the DS strategy increases retained β-phase fraction (up to 5.2%) and promotes crystallite coarsening relative to the SS condition, without significantly altering bulk hardness. Surface morphology examined by SEM/EDS and AFM revealed a more homogeneous near-surface topography in the DS condition. Tribological results indicate that sliding speed governs steady-state friction and wear, with specific wear rates increasing progressively from 5.13 to 5.44 × 10⁻⁴ mm³·N⁻¹·m⁻¹ for SS and from 6.47 to 7.52 × 10⁻⁴ mm³·N⁻¹·m⁻¹ for DS across the investigated speed range. The DS specimens exhibited higher wear rates than the SS condition across all tested speeds, while steady-state COF values remained comparable between strategies, indicating that remelting-induced microstructural modifications affect material removal mechanisms without proportionally destabilizing the frictional regime. These findings suggest that in-layer laser remelting represents a process-integrated parameter with measurable consequences for surface integrity and tribological performance, though the generalizability of these results warrants validation across broader experimental conditions. Full article

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

Additively manufactured stainless steels are gaining considerable attention in the production of complex components, especially in the aerospace, food production, energy, and biomedical industries. Machining and achieving the desired surface properties of such materials remains a challenge. Abrasive waterjet machining technology appears to be one of the options due to the advantages it brings. Removing support structures and separating individual parts is also one of the possible applications of this technology. This study investigates the effects of process parameters for individual cut qualities (Q1–Q5) of abrasive waterjet on the surface properties of additively manufactured stainless steel (SS 316L) specimens, considering the different mechanical properties of the material due to the direction of layering of the material during its production. Experimental specimens were prepared by selective laser melting technology with parameters ensuring the best possible quality of the resulting part. The results of the study showed changes in the topography of the machined surface, especially in the roughness parameters. Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy analysis proved the presence of fragmented abrasive particles in the cut areas. Full article

This study evaluates the applicability of the Advanced Predictor of Electrical Parameters (APEP) methodology to predict the degradation of key electrical parameters in analog-to-digital converters (ADCs) exposed to ionizing radiation, from measurements performed on the non-radiated device. While the APEP method has previously been validated for discrete analog devices, its extension to complex mixed-signal components has not yet been explored. This work addresses this extension using the PRECEDER database. The APEP methodology, based on machine learning techniques, is enhanced through multivariable analysis tools. This study focuses on the Integral Non-Linearity (INL) parameter of the AD574 converter, widely utilized in the aerospace applications. The results demonstrate that the APEP method can be successfully extended to ADCs, improving prediction performance with the incorporation of multiple electrical parameters which are non-radiated measurements. This new improvement is supported by t-Distributed Stochastic Neighbour Embedding (t-SNE), used as an exploratory analysis to reveal non-linear relationships among parameters that are not evident through univariate analysis. Overall, these findings confirm the potential of the multivariable APEP method to reduce the need for costly and destructive radiation testing, contributing to lower validation costs in space environments. Full article

Additive manufacturing (AM) has emerged as an enabling technology for producing lightweight and geometrically complex components in aerospace applications. This study investigates alternative manufacturing routes for a critical servo bracket used in a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) platform, aiming to comparatively evaluate their environmental, economic, and operational performance within a life cycle perspective. The servo bracket was manufactured using CNC milling, Selective Laser Melting (SLM), and Metal Extrusion Additive Manufacturing (MEX/M) and the three routes were assessed using Life Cycle Assessment (LCA), Life Cycle Cost (LCC), and process-based indicators, including production time and production process. The results indicate that CNC milling exhibits the highest carbon footprint per part (156.3 kg CO₂-eq.), mainly due to aluminium chip waste, whereas electricity consumption is the dominant contributor in SLM. Production times were 8.9 h for CNC, 52.7 h for SLM, and 71.6 h for MEX/M. From an economic perspective, CNC provides the lowest unit cost, while SLM is associated with the highest cost due to machine depreciation. Overall, the findings highlight distinct trade-offs between conventional and metal additive manufacturing routes and provide a life cycle-based decision framework for selecting suitable manufacturing strategies for VTOL UAV structural components. Full article

Laser beam powder bed fusion (PBF-LB) enables the fabrication of complex rotational metallic components for aerospace applications, such as engine exhaust nozzles and combustion liners, but the localized thermal cycles inherent to the process often lead to residual stress accumulation and geometry-dependent distortion, particularly in low-stiffness and open structures. This study investigates the thermo-mechanical response of three representative 316L stainless steel rotational geometries—dumbbell-shaped, cylindrical, and drum-shaped—in both closed and open configurations using a transient, fully coupled thermo-mechanical finite element model validated by high-resolution three-dimensional deformation measurements. The results reveal pronounced geometry- and size-dependent distortion mechanisms: for closed structures, the drum-shaped geometry exhibits the largest radial contraction and stress concentration due to its larger characteristic size and lower stiffness, with a maximum deformation of approximately 0.14 mm, whereas the dumbbell-shaped and cylindrical geometries show smaller and more uniform deformations of about 0.09 mm and 0.12 mm; open configurations experience substantially amplified distortion, with both the magnitude and vertical location of bulging governed by geometric stiffness and substrate constraint, and the open drum-shaped structure reaching a maximum displacement of approximately 1.6 mm. These findings clarify how geometric size and stiffness control stress relaxation and shape stability in PBF-LB-fabricated rotational components and provide transferable guidance for geometry-informed design and distortion mitigation in high-precision additive manufacturing. Full article

Traditional reliability engineering paradigms, originally designed to prevent physical component failures, are facing a fundamental crisis when applied to today’s software-intensive and autonomous systems. In domains like aerospace, critical risks no longer stem solely from the aleatory uncertainty of hardware breakdowns, but increasingly from the deep epistemic uncertainty inherent in complex systematic interactions and non-deterministic algorithms. This paper reviews the historical evolution of reliability engineering, tracing the progression through the Statistical, Physics-of-Failure, and Prognostics Eras. It argues that while these failure-centric frameworks perfected the management of predictable risks, they are structurally inadequate for the “unknown unknowns” of modern complexity. To address this methodological vacuum, this study advocates for an imperative shift towards a fourth paradigm: the Resilience Era. Grounded in the principles of Safety-II, this approach redefines the engineering objective from simply minimizing failure rates to ensuring mission success and functional endurance under uncertainty. The paper introduces uncertainty control (UC) as the strategic successor to uncertainty quantification (UQ), proposing that safety must be architected through behavioral constraints rather than prediction alone. Finally, the paper proposes a new professional identity for the practitioner: the system resilience architect, tasked with designing adaptive architectures that ensure safety in an era of incomplete knowledge. Full article

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely tied to software engineering. As software becomes a critical component of physical systems, such as vehicles, appliances, and production plants, bridging the gap between software engineering and other disciplines, such as mechanical, electrical, and civil engineering, becomes essential. Despite its potential, MBSE is still in its early stages when it comes to integrating executable models of physical systems into engineering environments. The purpose of this research is to assess the present capabilities of MBSE by identifying existing missing links, thereby enabling prospective users to make well-informed decisions about its integration into organizational processes. In this analysis, it is important to have a comprehensive view of the complexity of MBSE across different disciplines to obtain an overall picture. In addition to identifying open challenges, we present three critical gaps in the MBSE practice through a comprehensive demonstration case: limited tool interoperability and model integration, modeling language limitations, and dependence on a specialized workforce. Current studies largely view MBSE as the most applicable and effective for the design phase of the system life cycle. Yet, to capture MBSE in its entirety, its principles must be applied throughout the whole system life cycle. Full article

Promoting the development of aerospace vehicles toward structural–functional integration and intelligent sensing is a key strategy for achieving lightweight, high-reliability, and autonomous operation and maintenance of next-generation aircraft. However, traditional external sensors face significant limitations because of their bulky size, installation challenges, and incompatibility with aerodynamic surfaces. These issues are particularly pronounced on complex, high-curvature substrates, where achieving conformal bonding is difficult, thus restricting their application in critical components. In this study, aerosol jet printing (AJP) was employed to directly fabricate silver nanoparticle-based temperature sensors with real-time monitoring capabilities on the surface of high-curvature stainless steel sleeves, which serve as typical engineering components. This approach enables the in situ manufacturing of high-precision conformal sensors. Through optimized structural design and thermal treatment, the sensors exhibit reliable temperature sensitivity. Microscopic characterization reveals that the printed sensors possess uniform linewidths and well-defined outlines. After gradient sintering at 250 °C, a dense and continuous conductive path is formed, ensuring strong adhesion to the substrate. Temperature-monitoring results indicate that the sensor exhibits a nearly linear resistance response (R² > 0.999) across a broad detection range of 20–200 °C. It also demonstrates high sensitivity, characterized by a temperature coefficient of resistance (TCR) of 2.15 × 10^−3/°C at 20 °C. In repeated thermal cycling tests, the sensor demonstrates excellent repeatability and stability over 100 cycles, with resistance fluctuations kept within 0.5% and negligible hysteresis observed. These findings confirm the feasibility of using AJP technology to fabricate high-performance conformal sensors on complex surfaces, offering a promising strategy for the development of intelligent structural components in next-generation aerospace engineering. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

Radar systems serve as foundational components in both civil and military aerospace infrastructures. Modern radar must not only distinguish between detection and non-detection but must also classify detected objects. Signal processing increasingly integrates machine learning models into complex systems, such as radar. Additionally, developments have fused signal processing with quantum computing, creating an emerging field of research. This paper examines the applicability of quantum machine learning models for radar signal classification, focusing on the impact of Ansatz depth on expressibility. Multiple challenges arise due to the immature state of noisy intermediate-scale quantum hardware and the computational complexity of quantum circuit simulation. Nonetheless, results indicate that shallow Ansätze with fewer than 70 gates are sufficient to achieve the maximum available performance per data-encoding operation. Full article

Radially bolted cylindrical–cylindrical shell joints are critical load-bearing components in aerospace vehicles. These joints experience complex thermo–mechanical environments during flight, where aerodynamic heating and mechanical loads jointly induce nonlinear deformation and stiffness variation through evolving interfacial contact states. To elucidate these mechanisms, this study develops a sequentially coupled thermo–mechanical finite-element framework to analyze the stiffness evolution of RBCCSJs under transient heating and combined mechanical loads (tension, compression, and bending). The results show that the global stiffness evolves through distinct contact-controlled stages (sticking → microslip → macroslip → mechanical bearing), producing pronounced nonlinear stiffness troughs spanning over two orders of magnitude. Under tension and bending, stiffness peaks during full sticking and decreases with slip, whereas under compression, it recovers earlier due to its end-face-bearing formation. Transient heating introduces two competing effects, thermal-expansion-induced frictional stiffening during short-term heating and temperature-dependent material softening during sustained exposure, leading to a 19.2–34% reduction in stiffness under steady thermal conditions. These findings clarify the dominant role of contact-state evolution and thermo–mechanical coupling in joint behavior and provide a quantitative analytical basis for enhancing the stiffness reliability and design optimization of aerospace bolted assemblies operating in transient thermal environments. Full article

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. Full article