Search Results (513)

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

With the growing adoption of 3D stacked memory structures, precise alignment and overlay control have become critical for multi-layer overlay accuracy. The metrology accuracy and stability of alignment marks are crucial to ensuring optimal alignment and overlay performance. This study systematically investigates the contributions of two key alignment parameters—Wafer Quality (WQ) and Alignment Position Deviation (APD)—to the alignment model residue in 3D structures. Through experimental and simulation approaches, we analyze the interplay between WQ, APD and overlay performance. Results reveal that APD exhibits a stronger correlation with uncorrectable model residue, particularly under global process variations such as etch non-uniformity. Furthermore, APD sensitivity varies directionally (X/Y direction marks) and spatially (wafer edge versus center), highlighting the need for targeted mark designs in process-sensitive zones. These findings provide actionable insights for optimizing alignment strategies, mark designs and process monitoring throughout R&D, technology development and high-volume manufacturing phases. Full article

Tribological processes in extreme environments pose serious material challenges, requiring coatings that resist both wear and corrosion. This review summarizes recent advances in protective coatings engineered for extreme environments such as high temperatures, chemically aggressive media, and high-pressure and abrasive domains, as well as cryogenic and space applications. A comprehensive overview of promising coating materials is provided, including ceramic-based coatings, metallic and alloy coatings, and polymer and composite systems, as well as nanostructured and multilayered architectures. These materials are deployed using advanced coating technologies such as thermal spraying (plasma spray, high-velocity oxygen fuel (HVOF), and cold spray), chemical and physical vapor deposition (CVD and PVD), electrochemical methods (electrodeposition), additive manufacturing, and in situ coating approaches. Key degradation mechanisms such as adhesive and abrasive wear, oxidation, hot corrosion, stress corrosion cracking, and tribocorrosion are examined with coating performance. The review also explores application-specific needs in aerospace, marine, energy, biomedical, and mining sectors operating in aggressive physiological environments. Emerging trends in the field are highlighted, including self-healing and smart coatings, environmentally friendly coating technologies, functionally graded and nanostructured coatings, and the integration of machine learning in coating design and optimization. Finally, the review addresses broader considerations such as scalability, cost-effectiveness, long-term durability, maintenance requirements, and environmental regulations. This comprehensive analysis aims to synthesize current knowledge while identifying future directions for innovation in protective coatings for extreme environments. Full article

65 Mn is a high-quality carbon structural steel that exhibits excellent mechanical properties and machinability. It finds broad applications in machinery manufacturing, agricultural tools, and mining equipment, and is commonly used for producing mechanical parts, springs, and cutting tools. Fe901 is an iron-based alloy that exhibits excellent hardness, structural stability, and wear resistance. It is widely used in surface engineering applications, especially laser cladding, due to its ability to form dense and crack-free metallurgical coatings. To enhance the surface hardness and wear resistance of 65 Mn steel, this study employs a laser melting process to deposit a multi-layer Fe901 alloy coating. The phase composition, microstructure, microhardness, and wear resistance of the coatings are investigated using X-ray diffraction (XRD), optical microscopy, scanning electron microscopy (SEM), Vickers hardness testing, and friction-wear testing. The results show that the coatings are dense and uniform, without visible defects. The main phases in the coating include solid solution, carbides, and α-phase. The microstructure comprises dendritic, columnar, and equiaxed crystals. The microhardness of the cladding layer increases significantly, with the multilayer coating reaching 3.59 times the hardness of the 65 Mn substrate. The coatings exhibit stable and relatively low friction coefficients ranging from 0.38 to 0.58. Under identical testing conditions, the wear resistance of the coating surpasses that of the substrate, and the multilayer coating shows better wear performance than the single-layer one. Full article

With the rapid advancement of high-precision optical systems, increasingly stringent demands are imposed on the surface figure accuracy of optical components. The magnitude of residual stress in multilayer films directly influences the post-coating surface figure stability of these components, making the control of multilayer film stress a critical factor in enhancing optical surface figure accuracy. In this study, which addresses the process constraints and substrate damage risks associated with conventional annealing-based stress compensation for large-aperture optical components, we introduce an active stress engineering strategy rooted in in situ deposition process optimization. By systematically tailoring film deposition parameters and adjusting the thickness modulation ratio of TiO₂ and SiO₂, we achieve dynamic compensation of residual stress in multilayer structures. This approach demonstrates broad applicability across diverse optical coatings, where it effectively mitigates stress-induced surface distortions. Unlike annealing methods, this intrinsic stress polarity manipulation strategy obviates the need for high-temperature post-processing, eliminating risks of material decomposition or substrate degradation. By enabling precise nanoscale stress regulation in large-aperture films through controlled process parameters, it provides essential technical support for manufacturing ultra-precision optical devices, such as next-generation laser systems and space-based stress wave detection instruments, where minimal stress-induced deformation is paramount to functional performance. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

This study presents the development of a manufacturing process for a double-serpentine (DS) exhaust nozzle for unmanned aerial vehicles (UAVs) based on carbon fiber-reinforced silicon carbide matrix composites (C/SiCs). The DS nozzle is designed to reduce infrared emissions from hot exhaust plumes, a critical factor in enhancing stealth performance during UAV operations. The proposed nozzle structure was fabricated using a multilayer configuration consisting of an inner C/SiC layer for thermal and oxidation resistance, a silica–phenolic insulation layer to suppress heat transfer, and an outer carbon fiber-reinforced polymer matrix composite (CFRPMC) for mechanical reinforcement. The C/SiC layer was produced by liquid silicon infiltration, preceded by pyrolysis and densification of a phenolic-based CFRPMC preform. The final nozzle was assembled through precision machining and bonding of segmented components, followed by lamination of the insulation and outer layers. Mechanical and thermal property tests confirmed the structural integrity and performance under high-temperature conditions. Additionally, oxidation and ablation tests demonstrated the excellent durability of the developed C/SiC. The results indicate that the developed process is suitable for producing large-scale, complex-shaped, high-temperature composite structures for stealth UAV applications. Full article

Polymer residues can be reused in civil construction by partially replacing mineral aggregates in concrete, thereby reducing the extraction of natural resources. This study aimed to evaluate the use of powdered poly (ethylene terephthalate) (PET) and polyethylene (PE) residues, accumulated in shaving-mill filters during the extrusion of multilayer films used in food packaging, in the production of sealing masonry blocks. The PET/PE residues were characterized by Fourier Transform Infrared Spectroscopy (FTIR), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). Cylindrical specimens were produced in which part of the sand, by volume, was replaced with 10, 20, 30, 40 and 50% polymer residue. The cylindrical specimens were evaluated for specific mass, water absorption and axial and diametral compressive strengths. The 10% content provided the highest compressive strength. This formulation was selected for the manufacture of concrete blocks, which were evaluated and compared with the specifications of ABNT NBR 6136:2014. The concrete blocks showed potential for applications without structural function and were classified as Class C. The results, in line with previous investigations on the incorporation of plastic waste in concrete, underscore the promising application potential of this strategy. Full article

This study aims to establish a methodology that integrates experimental measurements with finite element analysis (FEA) to investigate the mechanical behavior and dynamic characteristics of soft–hard laminated composites fabricated via additive manufacturing (AM) under dynamic excitation. A hybrid AM technique was employed, using the PolyJet process based on stereolithography (SLA) to fabricate composite beam structures composed of alternating soft and hard materials. Initially, impact tests using a steel ball on cantilever beams made of hard material were conducted to inversely calculate the first natural frequency via time–frequency analysis, thereby identifying Young’s modulus and Poisson’s ratio. For the viscoelastic soft material, tensile and stress relaxation tests were performed to construct a Generalized Maxwell Model, from which the Prony series parameters were derived. Subsequently, symmetric and asymmetric multilayer composite beams were fabricated and subjected to impact testing. The experimental results were compared with FEA simulations to evaluate the accuracy and validity of the identified material parameters of different structural configurations under vibration modes. The research focuses on the time- and frequency-dependent stiffness response of the composite by hard and soft materials and integrating this behavior into structural dynamic simulations. The specific objectives of the study include (1) establishing the Prony series parameters for the soft material integrated with hard material and implementing them in the FE model, (2) validating the accuracy of resonant frequencies and dynamic responses through combined experimental and simulation, (3) analyzing the influence of composite material symmetry and thickness ratio on dynamic modals, and (4) comparing simulation results with experimental measurements to assess the reliability and accuracy of the proposed modeling framework. Full article

This review explores the intersection of Artificial Intelligence (AI) and Generative Design (GD) in engineering within the mechanical, industrial, civil, and architectural domains. Driven by advances in AI and computational resources, this intersection has grown rapidly, yielding over 14,000 publications since 2016. To map the research landscape, this review employed semantic search and Natural Language Processing, parsing 14,355 publications to ultimately select the 88 most relevant studies through clustering and topic modelling. These studies were categorised according to AI and GD techniques, application domains, benefits, and limitations, providing insights into research trends and practical implications. The results reveal a significant growth in the integration of advanced generative AI methods, notably Generative Adversarial Networks for direct design generation, alongside the continued use of genetic algorithms and surrogate models (e.g., Convolutional Neural Networks and Multilayer Perceptrons) to manage computational complexity. Structural and aerodynamic applications were the most common, with benefits including improvements in computational efficiency and design diversity. However, barriers remain, including data generation costs, model accuracy, and interpretability. Research opportunities include the development of generalisable foundation surrogate models, the integration of emerging generative methods such as diffusion models and large language models, and the explicit consideration of manufacturability constraints within generative processes. Full article

Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce output responses against challenge inputs based on the physical structure and intrinsic manufacturing variations of an integrated circuit (IC). These challenge-response pairs (CRPs) enable secure and reliable device authentication. Our work implements the Arbiter PUF (APUF) on Altera Cyclone IV FPGAs installed on the ALINX AX4010 board. The proposed APUF has achieved performance metrics of 49.28% uniqueness, 38.6% uniformity, and 89.19% reliability. The robustness of the proposed APUF against machine learning (ML)-based modeling attacks is tested using supervised Support Vector Machines (SVMs), logistic regression (LR), and an ensemble of gradient boosting (GB) models. These ML models were trained over more than 19K CRPs, achieving prediction accuracies of 61.1%, 63.5%, and 63%, respectively, thus cementing the resiliency of the device against modeling attacks. However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. APUFs are well-suited for device authentication due to their lightweight design and can produce a vast number of challenge-response pairs (CRPs), even in environments with limited resources. Our findings confirm that our approach effectively resists widely recognized attack methods to model PUFs. Full article

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (SACD) framework for industrial anomaly detection. SACD comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (FeaCali) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, SACD adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. FeaCali modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train SACD for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches. Full article

A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption. Full article

Powder bed fusion using a laser beam (PBF-LB) offers a suitable alternative to manufacturing Ti65 with intricate geometries and internal structures in hypersonic aerospace applications. However, issues such as undesirable surface roughness, defect formation, and microstructural inhomogeneity remain critical barriers to its wide application. In this study, a coupled discrete element method–computational fluid dynamics (DEM-CFD) model was utilized to investigate the spreading behavior of Ti65 powder in a multi-layer PBF-LB process. The macro- and microscopic characteristics of the powder beds were systematically analyzed across different layers and regions under various spreading velocities. The results show that the packing density and uniformity of the powder beds in multi-layer PBF-LB of Ti65 powder improves as the number of solidified layers increases. Poor bed quality is observed in the first two layers due to a strong boundary effect, while a stable and denser powder bed emerges from the fourth layer. The presence of a previously solidified region strongly influences its neighboring unsolidified areas, enhancing density in the upstream region and causing looser packing downstream. Additionally, due to the existence of a solidified region, the height of the powder bed progressively decreases along the spreading direction. Full article