Search Results (106)

Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. Future research directions include developing adaptive threshold learning mechanisms for varying industrial conditions and integration with surface defect detection capabilities to enhance comprehensive quality control in intelligent manufacturing systems. Full article

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and resilience of global logistics chains are increasingly under pressure. Additive manufacturing is regarded as a potentially viable solution to these problems, as it enables on-demand, on-site production, with reduced resource usage in production. Nevertheless, there are still significant challenges to be addressed, including the assurance of product quality and the optimisation of production processes with respect to time and resource efficiency. This article examines the potential of integrating digital twin methodologies to establish a fully digital and efficient process chain for on-site additive manufacturing. This study focuses on wire arc additive manufacturing (WAAM), a technology that has been successfully implemented in the on-site production of naval ship propellers and excavator parts. The proposed approach aims to enhance process planning efficiency, reduce material and energy consumption, and minimise the expertise required for operational deployment by leveraging digital twin methodologies. The present paper details the current state of research in this domain and outlines a vision for a fully virtualised process chain, highlighting the transformative potential of digital twin technologies in advancing on-site additive manufacturing. In this context, various aspects and components of a digital twin framework for wire arc additive manufacturing are examined regarding their necessity and applicability. The overarching objective of this paper is to conduct a preliminary investigation for the implementation and further development of a comprehensive DT framework for WAAM. Utilising a real-world sample, current already available process steps are validated and actual missing technical solutions are pointed out. Full article

22MnCrNiMo steel, a high-strength low-alloy material, is primarily used in the production of mooring chains for offshore oil platforms, offshore wind turbines, and ships. The application of additive manufacturing technology allows for the direct fabrication of seamless mooring chains. This paper investigates the selective laser melting (SLM) process parameters for 22MnCrNiMo mooring chain steel, analyzing the effects of different process parameters on the microstructure, phase composition, and mechanical properties of the steel. The experimental results demonstrate that under the laser parameters of 200 W laser power, 800 mm/s scanning speed, 30 μm layer thickness, and 110 μm scanning spacing, the SLM-formed parts exhibit the best comprehensive mechanical properties, with a microhardness of 513.2 HV0.5, a tensile strength of 1223 MPa, a yield strength of 1114 MPa, an elongation of 8.5%, and an impact energy of 127 J. This study reveals the microstructure evolution and the mechanism of enhanced mechanical properties in SLM-fabricated 22MnCrNiMo steel, providing a new approach for the preparation of high-performance mooring chains using 22MnCrNiMo steel. Full article

In the casting and stamping process of automobile, ship, aerospace, and other fields, improving the atomization quality of the spray release agent can effectively solve the problems of difficult film removal, low efficiency, and poor surface finish, and greatly improve the efficiency of production and manufacturing. The geometric model of the external mixing nozzle was constructed, and the calculation domain and grid were divided. The atomization flow field velocity, liquid film thickness, particle distribution, and cooling amount were calculated using fluid simulation software. Finally, an experimental platform was set up for verification. With the increase in the distance between the iron plate and the nozzle, the velocity of the flow field decreases from the nozzle exit to the periphery, and the frequency distribution of D_60–70 increases gradually. With the increase in the pressure ratio (K), the particle velocity increases gradually, the liquid film thickness increases first, and then gently decreases, and the D_60–70 frequency distribution decreases. The influence of gas pressure on atomized particle velocity and liquid film thickness is greater than that of liquid phase pressure, and the ion velocity reaches the peak value when K = 2. When K = 1.5, the average thickness increment of absolute liquid film is small, the atomized particle diameter changes the least, the frequency distribution of D₆₅ particles is approximately the same, and the atomization effect is the most stable. When the spraying time is 1 s, the K value is larger, and the smaller the temperature drop will be. In 2–4 s, the change in K value has little influence on the cooling amount. Full article

Ship manufacturing is a critical backbone industry in China, where the nation leads on a global scale in terms of vessel completions and order volumes. However, the high volume of orders often imposes substantial processing loads, increases the risk of equipment failures, and exacerbates production bottlenecks. Despite the accumulation of significant amounts of data in this field, analyzing bottlenecks remains a persistent challenge, primarily due to the presence of heterogeneous, multi-source data and the lack of effective data integration mechanisms. The traditional approaches are largely limited to bottleneck detection, offering minimal capabilities in terms of deep analysis, traceability, and interpretability, which are crucial for comprehensive bottleneck resolution. Meanwhile, extensive knowledge remains underutilized, leading to analytical results that are overly reliant on expert experience and lacking in interpretability. To address these challenges, this research proposes a graph-retrieval-based bottleneck mining method for ship manufacturing, employing large language models and a knowledge graph. The approach integrates a data-driven “turning point” mechanism for dynamic bottleneck detection and the manufacturing process knowledge graph, consisting of process subgraphs and 5M1E (Man, Machine, Material, Method, Measurement, Environment) specification subgraphs. Furthermore, a question-answering chain is introduced to enhance the interaction between the LLMs and the knowledge graph, improving the retrieval and reasoning capabilities. Using practical production data from a Shanghai ship thin plate production line, our method demonstrates a superior performance compared to that of four existing models, validating its effectiveness in throughput bottleneck analysis. This approach provides a scalable and efficient solution for analyzing complex bottleneck issues in industrial production, contributing to enhanced manufacturing efficiency and digital transformation. Full article

Vessel speed reduction measures are a management tool used to reduce the risk of whale–ship strikes and mitigate their impacts. Large ships and other commercial vessels are required to publicly share tracking information, including their speed, via the Automatic Identification System (AIS), which is commonly used to evaluate compliance with these measures. However, smaller vessels are not required to carry AIS and therefore are not as easily monitored. Commercial off-the-shelf marine radar is a practical solution for independently tracking these vessels, although commercial target tracking is typically a black-box process, and the accuracy of reported speed is not available in manufacturer specifications. We conducted a large-scale measurement campaign to estimate radar-reported speed error by comparing concurrent radar- and AIS-reported values. Across 3097 unique vessel tracks from ten locations, there was strong correlation between radar and AIS speed, and radar values were within 1.8 knots of AIS values 95% of the time. Smaller vessels made up a large share of the analyzed tracks, and there was no significant difference in error compared to larger vessels. The results provide error bounds around radar-reported speeds that can be applied to vessels of all sizes, which can inform vessel-speed-monitoring efforts using radar. Full article

Houston, Texas, with its large-scale industrial activities, serves as a national hub for petrochemical processing and chemical feedstock production, making it a unique emission region for volatile organic compounds (VOCs) and production-related emissions. These emissions can be associated with industrial activities, including solvent usage and production to manufacture consumer products such as volatile chemical products. To support the Houston-based Dept. of Energy’s Atmospheric Measurement Radiation program-led Tracking Aerosol Convection ExpeRiment (TRACER) projects, VOCs were measured at the San Jacinto Battleground State Historic Site during September 2021 and 2022. The observed VOC mixing ratios reveal unique emission signatures for select VOCs, including benzene, toluene, acetone, and isoprene. Routine nighttime enhancements of these compounds exceeded the urban background, with mixing ratios increasing by up to 20 ppbv per hour and persisting for up to 6 h, suggesting that emissions from local industrial activities near the Houston Ship Channel (HSC) are impacting the site. For example, mixing ratios exceeding 15 ppbv for at least one VOC were observed on 58% of nights (n = 32 nights), with 19 nights (~35%) having two or more VOCs with mixing ratios above 15 ppbv. For select peak emission events, the NOAA dispersion model estimated plume transport across parts of the urban system, suggesting that VOCs from the HSC may impact local air quality. This study highlights the importance of VOC-related emissions from industrial production and supply chains in contributing to total VOC emissions in urban areas like Houston, Texas. Full article

The digitalization of shipbuilding processes has become an important trend in modern naval construction, enabling more efficient design, assembly, and maintenance operations. A key aspect of this digital transformation is traceability, which ensures that every component and step in the shipbuilding process can be accurately tracked and managed. Traceability is critical for quality assurance, safety, and operational efficiency, especially when it comes to identifying and addressing defects that may arise during construction. In this context, defect traceability plays a key role, enabling manufacturers to track the origin, type, and evolution of issues throughout the production process, which are fundamental for maintaining structural integrity and preventing failures. In this paper, we focus on the detection of defects in minor and simple pre-assemblies, which are among the smallest components that form the building blocks of ship assemblies. These components are essential to the larger shipbuilding process, yet their defects can propagate and lead to more significant issues in the overall assembly if left unaddressed. For that reason, we propose an intelligent approach to defect detection in minor and simple pre-assembly pieces by implementing unsupervised learning with convolutional autoencoders (CAEs). Specifically, we evaluate the performance of five different CAEs: BaseLineCAE, InceptionCAE, SkipCAE, ResNetCAE, and MVTecCAE, to detect overshooting defects in these components. Our methodology focuses on automated defect identification, providing a scalable and efficient solution to quality control in the shipbuilding process. Full article

One of the most widely used processes in ship hull plate manufacturing is the flame forming process (FFP). In this work, the fabrication of saddle-shaped specimens with FFP using a spiral irradiating pattern is studied experimentally. The deformation of the deformed plates by FFP based on the spiral irradiating pattern is affected by process parameters such as the pitch of spiral passes (PSP), the radius of the starting circle (RSC), and the number of irradiation passes (NIP). However, in this work, the effects of process parameters on the deformation of SSS are statistically examined by the design of experiment (DOE) method based on response surface methodology (RSM). The experimental and statistical results show that the deformation of flame-formed SSS increases with the increase in RSC and NIP and the decrease in PSP. In addition, the results of the optimization procedure demonstrate that the maximum value of deformations of flame-formed saddle-shaped specimens is achieved by adjusting the process parameters as follows: PSP = 10 mm, RSC = 75 mm, and five NIPs. Full article

Realizing accurate control of ship target information in complex marine environments is of great significance for maintaining marine environment security and safeguarding maritime sovereignty. With the rapid development of material technology and manufacturing industry, the types and styles of ships are increasing, and the distribution of multi-type ships on the sea is widespread. How to realize the accurate detection and identification of dynamic multi-type ship targets in the complex marine environment is an important and difficult problem that needs to be solved urgently in current marine environment detection. In this paper, an improved YOLOv11 ship target detection algorithm is proposed, which firstly utilizes the improved EfficientNetv2 network to replace the original backbone network of YOLOv11 to improve the learning ability of ship features under complex sea conditions; in order to solve the problem of interference by moving objects at sea when detecting dense ship targets and reduce the problems of missing detection and false alarms, the algorithm borrows from ConvNext block idea in the process of a neck feature pyramid network fusion; the algorithm introduces the WIoU loss function, which compensates for the effect of the small number of pixels of the small target in the process of regression loss computation, so as to improve the network’s performance in detecting small targets. In order to test the network performance in actual application scenarios, the article builds a visible ship target dataset, including complex background, occlusion and overlap, small targets, and other factors. Through experimental verification, the detection accuracy of the improved algorithm is improved by 5.6% compared with the original algorithm, and compared with typical algorithms in terms of detection accuracy, speed, and number of parameters, ablation experiments are designed to comprehensively validate and analyze the algorithm’s performance. Full article

Thin-walled components are extensively utilized in the aviation, aerospace, shipping, and nuclear energy industries due to their advantages of being lightweight and easily integrated. With an increased design quality and complexity of structures, thin-walled components have rendered traditional offline machining state prediction techniques inadequate for meeting the rising demands for machining quality. In recent years, advancements in intelligent manufacturing have led to the emergence of intelligent monitoring technologies that offer new solutions for enhancing the machining quality. This review categorizes technologies into online signal collection, state recognition, and intelligent decision-making, based on the implementation processes of intelligent monitoring. It summarizes the roles and current development status of various technologies within intelligent monitoring and outlines the existing challenges associated with each technology. Finally, the review discusses the challenges and future development trends of intelligent monitoring technology. Full article

In the marine industry, the search for sustainable methods, materials, and processes, from the product’s design to its end-of-life stages, is a necessity for combating the negative consequences of climate change. In this context, the lightening of products is essential in reducing their environmental impact throughout their life. In addition to lightening through design, lightweight materials, especially plastic-based composites, will need to be used in new and creative ways. The material extrusion technique, one of the additive manufacturing methods, is becoming more widespread day by day, especially in the production of objects with complex forms. This prevalence has not yet been reflected in the marine industry. In this study, the performances of plastic composite propellers produced by the material extrusion technique is investigated and discussed comparatively with the help of both hydrodynamic and structural tests carried out in a cavitation tunnel and mechanical laboratory. The cavitation tunnel test and numerical simulations were conducted at a range of advance coefficients (J) from 0.3 to 0.9. The shaft rate was kept at 16 rps. The thrust and torque data were obtained using the tunnel dynamometer. Digital pictures were taken to obtain structural deformation and cavitation dynamics. The structural performance of the propellers shows that an aluminum propeller is the most rigid, while a short carbon fiber composite propeller is the most flexible. Continuous carbon fiber composite has high strength and stiffness, while continuous glass fiber composite is more cost-effective. In terms of the hydrodynamic performance of the propellers, flexibility reduces the loading on the blade, which can result in thrust and torque reduction. Overall, the efficiency of the composite propellers was similar and less than that of the rigid aluminum propeller. In terms of weight, the composite carbon propeller containing continuous fiber, which is half the weight of the metal propeller, is considered as an alternative to metal in production. These propellers were produced from a unique composite consisting of polyamide, one of the thermoplastics that is a sustainable composite material, and glass and carbon fiber as reinforcements. The findings showed that the manufacturing method and the new composites can be highly successful for producing ship components. Full article

This paper introduces a hybrid optimization method that leverages either linear programming (LP) or a genetic algorithm (GA) based on the problem size to enhance the parallel additive manufacturing (AM) process for ship models. The LP ensures optimality but can experience exponential increases in the computation time as the problem size grows. To address this limitation, the GA is employed for larger problems, providing optimal solutions within reasonable quality and time constraints. The method optimizes the module allocation to AM machines and determines the build processing sequence for each machine, while also considering the availability of workers preparing for consecutive module production. Applied to a case study, the proposed method achieves a 14% reduction in the completion time compared to a heuristic method from a previous study. Furthermore, the method is validated by benchmarking against the heuristic method across various problem sizes, consistently demonstrating superior performance. Full article

There are two main areas in which the Internet of Ships (IoS) can help: firstly, the production stage, in all its phases, from material bids to manufacture, and secondly, the operation of the ship. Intelligent ship management requires a lot of information, as does the shipbuilding process. In these two phases of the ship’s life cycle, IoS acts as a key to the keyhole. IoS tools include sensors, process information and real-time decision-making, fog computing, or delegated processes in the cloud. The key point to address this challenge is the design phase. Getting the design process right will help in both areas, reducing costs and making agile use of technology to achieve a highly efficient and optimal outcome. But this raises a lot of new questions that need to be addressed: At what stage should we start adding control sensors? Which sensors are best suited to our solution? Is there anything that offers more than simple identification? As we begin the process of answering all these questions, we realize that a Computer Aided Design (CAD) tool, as well as Artificial Intelligence (AI), mixed in a single tool, could significantly help in all these processes. AI combined with specialized CAD tools can enhance the sensitization phases in the shipbuilding process to improve results throughout the ship’s life cycle. This is the base of the framework developed in this paper. Full article