Life Cycle Assessment (LCA) of additive manufacturing (AM) evaluates the environmental impacts associated with each stage of the process, from raw material extraction to end-of-life disposal. Unlike conventional manufacturing, AM offers significant advantages, such as reduced material waste, optimized designs for lightweight structures, and localized production, which can decrease transportation emissions. However, its environmental benefits are context-dependent, as energy-intensive processes like laser powder bed fusion or high reliance on specific materials can offset these gains. LCA provides a comprehensive framework to assess these trade-offs, guiding sustainable decision-making by identifying hotspots in energy use, material efficiency, and recyclability, ultimately driving innovation towards greener AM practices. This research conducted a cradle-to-gate study of a cylindrical dog-bone tensile specimen. The life-cycle inventory data were obtained from Ecoinvent for conventional manufacturing, while data from the literature review and our research were employed for laser-based powder bed fusion. The results obtained show that the additive manufacturing process is more environmentally friendly. Although the environmental impact is minor, this process consumes a large amount of energy, mainly due to the atomization process and the high laser power. Regarding the mechanical response, AM reduced the ductility but increased the yield strength and achieved the same fracture strength.
Recycling high-density polyethylene (HDPE) is crucial to addressing plastic waste challenges. This study investigates the mechanical properties of blends composed of HDPE, polybutylene terephthalate (PBT), and polyamide 6 (PA6). Blends with varying HDPE content (0, 70, 80, 90, and 100%) were analyzed using injection molding to determine their impact toughness and structural characteristics. PBT and PA6 (blended in a 50:50 ratio) were combined with HDPE to create composites with enhanced properties. Testing included unnotched impact strength analysis and scanning electron microscopy (SEM). HDPE, a flexible thermoplastic, was paired with PBT and PA6, known for their strength and heat resistance, to produce a blend with superior mechanical performance. Results reveal that incorporating HDPE enhances the impact toughness of the composites compared to the pure PBT/PA6 blend, offering promising potential for many diverse applications in materials engineering in the automotive industry, household products, and protective casings of electronic products.
Carbon nanotubes (CNTs) are essential for providing polymers with mechanical reinforcement and multifunctional properties. This study investigated two groups of nitrile butadiene rubber (NBR) nanocomposites containing single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs), respectively. SWCNTs were purified to remove appro-ximately 20 wt.% of impurities, and both CNTs were modified with polyethylene glycol tert-octylphenyl ether (Triton X-100) before emulsion compounding and 2-roll milling with NBR. MWCNTs were found to disperse in the elastomer matrix relatively uniformly, while SWCNTs formed aggregates. Consequently, NBR/MWCNT nanocomposites exhibited superior mechanical properties, e.g. a tensile strength of 10.8 MPa at 4.02 vol.% MWCNTs, compared to 5.6 MPa for NBR/SWCNT nanocomposites. Additionally, NBR/MWCNT nanocomposites exhibited more remarkable electrical conductivity and swelling resistance to toluene. The diameter of elastomer macromolecules (0.2–0.5 nm) is close to that of SWCNTs (1–2 nm), and their single graphene wall with a hollow structure makes SWCNTs almost as flexible as elastomer macromolecules. This similarity suggests that SWCNTs should be treated as a special type of polymer. SWCNTs cannot disperse as uniformly as MWCNTs in the elastomer matrix, likely due to their smaller size and lower sensitivity to mechanical shearing during the emulsion compounding and 2-roll milling process.
Quantum spin liquids of frustrated magnets are among the most attractive and basic systems in physics. Frustrated magnets exhibit exceptional properties as insulators and metals, making them advanced materials that represent materials for future technologies. Therefore, a reliable theory describing these materials is of great importance. The fermion condensation theory provides an analytical description of various frustrated quantum spin liquids capable of describing the thermodynamic and transport properties of magnets based on the idea of spinons, represented by chargeless fermions filling the Fermi sphere up to the Fermi momentum pF . We show that the low temperature thermodynamic of Sr3CuNb2O9 in magnetic fields is defined by strongly correlated quantum spin liquid. Our calculations of its thermodynamic properties agree well with recent experimental facts and allow us to reveal their scaling behavior, which is very similar to that observed both in heavy-fermion metals and in frustrated magnets or insulators. We demonstrate for the first time that Sr3CuNb2O9 belongs to the family of strongly correlated Fermi systems that form a new state of matter.
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.
