The growing demand for sustainable materials in the automotive industry has prompted research into natural fiber-reinforced composites. To reduce carbon footprints and enhance product sustainability, the sectors increasingly focus on renewable and biodegradable materials. Composites made from natural fibers, such as coir and hemp, offer a promising solution for creating lightweight, high-performance components with a reduced environmental impact.In this study, an experimental investigation was conducted to examine the impact of single and hybrid and treated and untreated fibers, on the properties of epoxy-based composites. Untreated hemp fiber with treated Coir fiber was used for the research. The composites were fabricated through the open mould hand lay-up technique. Samples were prepared by randomly dispersing the fibers in the epoxy matrix before pouring them into the respective moulds prepared according to ASTM standards. Tensile, impact, and hardness tests were conducted on the cured samples to determine their mechanical properties, while a scanning electron microscope was used to evaluate the fractured surface. Water absorption tendencies were also determined. The results showed that the sample denoted as 5CF wt.% had the best property combination with tensile strength (32.4 MPa), tensile modulus (11.9 GPa), flexural strength (167.0 MPa), and impact strength (46.8 kJ/mm2). It was discovered that hemp fiber-based composites were not enhanced properly due to lack of fiber surface modifications. Though optimum results were obtained from treated coir fiber-based single/distinct composite, untreated hemp fiber was discovered to aid some flexural modulus and hardness properties in the hybrid composite based on the best results obtained in its distinct-based composite. Therefore, untreated hemp fiber can be used in hybrid form with treated coir fiber where one of the fibers is scarce or when fiber surface medication is difficult to achieve. Thus, the results showed that 5CH-based composites are the most suitable composition for automotive components development where high-mechanical properties are essential.
The diagnosis of paper breakage faults during the papermaking process is of great significance for improving product quality and maintaining stability in the production process. This paper develops a cross-condition transfer learning fault diagnosis model. This study proposes a fault diagnosis method based on transfer learning to address the issue of single-condition diagnostic models performing poorly when applied to different conditions..This method uses both parameter transfer and feature transfer to diagnose faults across different conditions. At the same time, in response to the issue of insufficient small sample operating data, we introduce federated learning technology to explore the impact of model compression rates on the diagnostic accuracy of the federated global model during the federated model training process. The results indicate that compared to single operating condition models, fault diagnosis performance based on transfer learning across different operating conditions has improved. The diagnostic model based on feature transfer performs even better, achieving accuracy rates of 98.31%, 94.64%, and 96.43% under different transfer tasks, allowing for accurate classification of the majority of samples. Additionally, the federated learning method provides an effective solution for fault diagnosis in small sample operating conditions, and an appropriate model compression rate can ensure diagnostic accuracy while protecting data privacy.
A new combat strategy that enables coordinated operations of gliding aircraft clusters for multi-target strikes imposes higher demands on the coordination, real-time responsiveness, and strike accuracy of gliding aircraft clusters. Due to the high speed and large inertia characteristics of gliding aircraft, traditional trajectory planning methods often face challenges such as long computation times and difficulty in responding to dynamic environments in real-time when dealing with large-scale gliding aircraft clusters. This paper proposes a distributed cooperative trajectory planning method for multi-target strikes by gliding aircraft clusters to address this issue. By introducing a multi-objective distributed real-time trajectory planning approach based on Multi-Agent Deep Deterministic Policy Gradients (MADDPG), the gliding aircraft execute distributed cooperative trajectory planning based on the trained model. Due to its robust real-time performance, the gliding aircraft do not need to recalculate trajectories for different initial positions of the cluster. Simulation results show that the average error between the gliding aircraft cluster and the target point is 2.1 km, with a minimum error of 0.06 km and a hit rate of 96.6%, verifying the significant advantages of this method in real-time planning capability and strike accuracy.
The steam turbine is a rotating device subject to axial and radial shaft shifts that can induce vibrations during operation. Tools such as monitoring systems and proximity probe sensors are essential to monitoring these vibrations. High vibrations affect the machine’s performance, increasing the risk of malfunctions and reducing its lifespan, and also pose risks to operational and maintenance personnel. The intensified vibrations in the bearing pedestals signify the underlying issues with the machine’s normal operation. Consequently, problems such as rotor imbalance, coupling misalignment, mechanical looseness, material failure, and bent shaft may be caused. In the current study, the latest field-proven automatic diagnostic of rotary equipment (ADRE 408) data acquisition system is installed by Bentley Nevada to investigate the root cause of high vibration. This advanced diagnostic system facilitates a comprehensive assessment, enabling us to effectively identify and address underlying problems. Hence, the current research includes a thorough diagnosis of the underlying problems to attenuate the risks of high vibrations in the steam turbine, coupled with strategic maintenance planning and corrective actions.
This paper gives a comprehensive review of scientific interests and current methodologies of artificial intelligence applied to advanced material design and discovery by taking into account multiple sustainable design criteria such as functionalities, costs, environmental impacts, and recyclability. The main research activities include predicting material properties, compositions, and structures with data mining, new material discovery, hybrid modeling approaches combining AI techniques and classical computational formulations based on physical and chemical laws, and multicriteria optimization of materials. Based on this review, a short analysis is provided on the perspectives of this research area in the future, aiming at creating an everything connected material life cycle with real-time traceability systems
Pressure garment therapy (PGT) and silicone gel sheeting (SGS) predominate non-invasive interventions for burn injuries, but the market lacks a composite solution combining pressure garment fabric (PGF) and medical-grade silicone (e.g. Biopor®AB) for multi-therapeutic efficacy. To address this gap, a versatile composite dressing of PGF-Biopor®AB was developed. PGF-Biopor®AB incorporates dual PGF-SGS therapy, mechanotherapy, and active moisture management, to facilitate recovery of hypertrophic subsidiary structures. The PGF structure enables the application of PGT, while the Biopor®AB silicone characteristics enforce silicone gel therapy (SGT). The PGF-SGS efficacy optimization not only reduces tension but also facilitates water vapor and oxygen penetration, along with hydration of the stratum corneum. Mechanotherapy, involving tension-shielding and pressure redistribution, promotes the reorganization of the collagen-fiber network. For active moisture management, the incorporation of a microchannel structure with active nylon absorbency facilitates effective moisture control through water absorption, retention, and cellular pathways of transport. In this study, the microscale features in the structure were further investigated. Under ISO 10993-5 standard, an over 70% cell viability in 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay containing the L929 cell line verified the enhanced cell growth and inhibited proliferation, endorsing the safe usage of PGF-Biopor®AB. Patient studies of one-month efficacy in both high and low-cell-density samples and an early scarless healed wound suggest that over 70% cell viability is sufficient for optimal scar therapeutics. The multifaceted scar repair roles are fulfilled by addressing persistent inflammation, insufficient oxygenation, low levels of perfusion, and scar-healing tension, hence realising the multi-therapeutic efficacy of the composite dressing.
This study explores the impact of energy input and build orientation on the anisotropic mechanical and functional properties of Ti-rich Nitinol (NiTi) produced via electron beam powder bed fusion (PBF-EB), integrated with layerwise in-situ monitoring of the melted surface via backscatter electron detection (ELO). NiTi, a binary alloy of nickel and titanium, exhibits shape memory and superelasticity, making it widely used in biomedical applications and sustainable technologies. PBF-EB, particularly with ELO, is highlighted for its advantages in producing crack-free NiTi with tailored microstructures. The investigation reveals that energy input significantly influences microstructure phases, with higher energy promoting increased evaporation of Ni and enhancing Ti-rich Ti2Ni precipitates, allowing for tailored material properties. Build orientation also proves crucial, impacting mechanical responses and functional properties. The 0° orientation yields the hardest mechanical response with the highest ultimate tensile strength (UTS) and the highest strain recovery ratio while the 45° orientation shows improved ductility but lower UTS. The influencing factors towards the formation of the anisotropic material properties are explained and the potential of tailoring the NiTi properties for specific applications by controlling energy input and build orientation in the PBF-EB process are underlined. These insights offer valuable criteria for designing innovative NiTi parts.
The structure of the drying section in papermaking process is complex and too compacted to install sensors. In order to monitor the parameters in dynamic and manage the process practically with virtual simulations instead of physical experiments, a digital twin-based process parameter visualization model is constructed in this study. Regarding to the possible missing data in the modeling framework, it is proposed to combine industrial data, and knowledge of mechanism with intelligent algorithms to fill in the missing parameters. Upon which, a digital twin-based data visualization model is established using CADSIM Plus simulation software. Both of the knowledge -based mechanism solution model and the random forest-based parametric prediction model perform well, and the predicted parameters can support the digital twin visualization model in CADSIM Plus. Visual modeling of surface condenser in the paper drying section was realized for example, and results show that the model is capable of monitoring the dynamic changes of parameters in real time, so as to support the optimization and decision making of papermaking process such as formation, drying, et al.
The rapid development of manufacturing sector has created a platform for implementing novel technologies such as additive manufacturing (AM). AM or 3D printing, has generated a lot of interests in biomedical applications during the last decade with a variety of novel printed polymeric materials. 3D printing fabricates 3D object with layer-by-layer processing through computer-controlled programming software. It has innumerable applications including electronics, aerospace engineering, automobile industry, architecture and medical sectors. One of the most demanding sectors of 3D printing is biomedical engineering applications such as medicines, drug delivery system, surgical instruments, orthopedics, scaffolds, implants etc. The clinical ramifications of AM-made healthcare goods are being catalyzed by recent developments in biomaterials. This review paper aims to explain the concept of 3D printing and its significance in developing polymeric materials for biomedical applications. An inclusive survey has been conducted on the various techniques involved in printing the biomedical devices. The proper selection of polymeric materials is important for biomedical applications, especially from 3D printing point of view and this vital parameter has been considered in this review paper. According to our findings, more breakthroughs in biomaterials, are required for the success and expansion of AM technology in the biomedical applications.
