Materials & Design最新文献

In the ever-increasing quest for alternative energy sources, hydrogen emerged as a promising green option, but efficient and economical production and management have been the primary constraints. Converting wastewater into H₂ and other forms of energy attracted significant attention in terms of simultaneously and sustainably managing both the wastewater and the energy generation problems. Microbial Electrolysis Cells (MEC) evolved recently as promising options for converting wastewater into H₂ and electricity but with serious constraints on scalability. The current research aims to explore design and manufacturing solutions to build structurally strong and electrochemically effective electrodes that can also lead to scalable MEC. Two designs based on the interdigitated and spiral electrode architectures are proposed and evaluated. The added design freedom with additive manufacturing by selective laser melting of specific alloys of choice is effectively utilised in physically prototyping the interdigitated and spiral electrode forms designed with controlled porosity constraints. Microstructural, electrochemical, and cell performance characterisations led to the understanding that the spiral electrode configuration with polypyrrole-coated stainless steel 316L anode is a promising design option for both longitudinal and lateral scale-up of the MEC.

Fiber composites must be evaluated to achieve correct use in various fields. Their properties, performance, condition, and integrity can be quickly predicted and optimized by machine learning (ML), after extensive training, compared with experiments and conventional computational simulations. In this document, papers on ML applications in fiber composites were collected and critically reviewed. It was revealed that kind learning environments have been primarily used. Supervised ML has been more frequently used than unsupervised ML, whereas some specific semi–supervised ML (e.g., reinforcement learning) or deep predictive control have been overlooked. Most ML applications have been successful on the laboratory scale and in the short term. Furthermore, the deployment of ML applications has been overlooked. In addition, retroactive feedback from the manufacturing of fiber and polymers to the manufacturing of composite laminates and structures was neglected. Accordingly, a control loop in the chain of manufacturing processes was discussed. Additionally, language processing tools and statistics were used to summarize and analyze the papers. Finally, it was proposed that multiscale modeling using ML and physics is a potential approach to advance predictions for future applications. Therefore, physicochemical interactions (van der Waals or electrostatic) from nanoscale can be included.

The instability of plastic flow in medium manganese steel was investigated through uniaxial tensile tests conducted at room temperature across various strain rates. The Portvin-Le Chatelier (PLC) effect was analyzed using statistical methods and a multifractal approach. The findings indicate that the zigzag stress–strain curves associated with instability plastic flow exhibit typical multifractal characteristics. Furthermore, the multifractal analysis quantifies the influence of strain rate and initial phase composition on the instability characteristics of the stress–strain curves. Specifically, it was observed that lower strain rates and austenite content correspond to a chaotic state, whereas higher strain rates and austenite content result in self-organizing critical dynamics. Finally, the underlying physical mechanisms driving these differing modes of behavior are discussed, with a focus on their relationship to strain rate and the initial austenitic phase fraction.

The model of the diffusion bonding pressure was established using the analysis calculation method and further verified by the finite element analysis (FEA) and the experiments. The normalized temperature was defined as the ratio of temperature and the melting point of the base materials. And the normalized bonding pressure was defined as the ratio of the applied bonding pressure and the yield strength at the bonding temperature. The contribution of the plastic deformation to the void closure expressed a linear relationship with the normalized bonding pressure, of which the slope was $\sqrt{3}$. The relationship was appropriate in TiAl6V4, pure copper C11000, aluminium alloy AA6061, stainless steel SUS 304, and Ni-based alloy Inconel 617 using analysis calculation. The diffusion bonding pressure design range can be summarized as σ_n = 0.05–0.577. Subsequently, the analytic computational model was verified by FEA. The results showed that the maximum stress was concentrated in the position of the void neck. And the linear relationship between the normalized bonding pressure and the bonded ratio was also tenable. There was a stable static error existed between FEA and the analytic computational analysis. Furthermore, the experimental verification showed the verification and accuracy of the analytic computational analysis results.

The increased interest in printed electronics necessitates the development of suitable sustainable substrates for them. In this study, the suitability of acetylated cellulose nanofiber (ACNF) films as substrates for printed electronics were examined through (I) the ink-substrates interaction, (II) print quality, and (III) electrical and (IV) mechanical properties of the printed pattern on the ACNF substrates. The results have been compared with cellulose nanofiber (CNF) and commercial reference material (CRM) substrates. The wetting of the silver nanoparticle (AgNP) ink on the ACNF substrate was found out to be excellent. The thickness of the printed pattern increased and the hole area fraction decreased with each consecutive layer of ink. The comparative investigations demonstrated that the electrical properties of the printed patterns were almost as good for 4 layers of ink on the ACNF substrate (1.6 Ω resistance) as the ones on the CRM substrate (1.2 Ω resistance). Additionally, the printed pattern on the ACNF substrate endured the adhesion and bending tests significantly better compared to the CRM substrate. Therefore, this study demonstrates that ACNF substrates could be a suitable candidate for printed electronics, which are more sustainable yet with similar functionalities in comparison with the conventional fossil based substrates.

The precise friction coefficient at interface determines the friction behavior in inertia friction welding (IFW), its model is the basis to precisely describe the friction behavior in the numerical simulation of IFW. To measure the friction coefficient related to the temperature, pressure, and sliding velocity, the IFW trials between 2219-O Al alloy and 304 stainless steel were conducted with the simultaneous measurement of temperature and angular velocity evolutions. Two types of friction coefficient models were established by considering whether the effects of temperature and friction pressure were independent. The S-(T&P) L model, which considered the couple effect of temperature and friction pressure, was recommended as the best suitable model with its very high accuracy and best simplicity. The proposed model was successfully used in simulating the temperature evolution of IFW between Al alloy and steel tubes. The effect of the influencing variables on the friction coefficient was also discussed.

Mass reduction is a concern both in mechanical engineering and living organisms to reduce energy and materials consumption. Lately, transfer of knowledge from biology to engineering has become easier thanks to additive manufacturing. Trabecular bone for example optimally adapts to the mechanical stress it undergoes. Mass reduction methods bio-inspired from trabecular architecture, proposed in the literature, are not always applicable for laser-based powder-bed fusion. In particular, bio-inspired mass reduction methods based on a material removal principle generate porosities throughout a solid initial part which can result in enclosed porosities that trap un-melted powders. Here, we propose a 3D depowderable bio-inspired mass reduction method. Powder removal was controlled in different samples through weighing and X-ray computed microtomography and the impact of the design strategy that ensures correct powder removal on mechanical performance (stiffness) was quantified. Mean performance difference is 10.35 % (between 1.82 % and 26.47 %). For stiff and light parts loaded in bending, the proposed method outperforms parts made of bulk steel alloy by leveraging an architecture bio-inspired from trabecular bone.

This study proposes a hybrid process method based on Cryogenic Minimum Quantity Lubrication and Ultrasonic Rolling Strengthening Process (CMQL-URSP) to improve the surface integrity and enhance the mechanical properties of blade surface. First, the principle of the CMQL-URSP hybrid process was described. Second, the TEM experiments were conducted to clarify the influence of the CMQL-URSP hybrid process on aircraft engine blades. Finally, the blade processing experiments were performed to demonstrate the effectiveness of the CMQL-URSP hybrid process on the blade surface micro-morphology, micro-hardness and residual stress. The results indicate that the CMQL-URSP hybrid process effectively overcomes the limitations inherent to the solitary application of URSP. The CMQL process inhibits work hardening, thereby allowing the URSP to achieve better low-plastic surface improvement. Grain refinement governed by the continuous dynamic recrystallization mechanism leads to the formation of nanocrystals, which significantly improves mechanical properties of blade surface. The CMQL-URSP hybrid process results in a reduction in surface roughness from Ra 2.644 μm to Ra 0.055 μm, an increase in surface residual compressive stress from 321 MPa to 657 MPa, and the formation of a reinforced layer with a depth of 3.7 mm on the blade, thereby enhancing the surface integrity of the blade.