Dual Reinforcement Research Articles

Semi-Supervised Graph Structure Learning via Dual Reinforcement of Label and Prior Structure.

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental assumption that the graph structure along which information propagates is perfect. However, the real-world graphs are inevitably incomplete or noisy, which violates the assumption, thus resulting in limited performance. Therefore, optimizing graph structure for GNNs is indispensable and important. Although current semi-supervised graph structure learning (GSL) methods have achieved a promising performance, the potential of labels and prior graph structure has not been fully exploited yet. Inspired by this, we examine GSL with dual reinforcement of label and prior structure in this article. Specifically, to enhance label utilization, we first propose to construct the prior label-constrained matrices to refine the graph structure by identifying label consistency. Second, to adequately leverage the prior structure to guide GSL, we develop spectral contrastive learning that extracts global properties embedded in the prior graph structure. Moreover, contrastive fusion with prior spatial structure is further adopted, which promotes the learned structure to integrate local spatial information from the prior graph. To extensively evaluate our proposal, we perform sufficient experiments on seven benchmark datasets, where experimental results confirm the effectiveness of our method and the rationality of the learned structure from various aspects.

Short-text topic modeling with dual reinforcement from internal and external semantics

Short-text topic modeling with dual reinforcement from internal and external semantics

Evaluation, tribological examination, and multi‐objective optimization of aluminum‐fly ash‐egg shell composites for sustainability

AbstractThis study addresses the technological challenge of enhancing the mechanical and tribological properties of Al 6082 alloy by incorporating fly ash (FA) and eggshell (ES) as reinforcing materials. The objective is to fabricate hybrid metal matrix composites using sand casting, with varying weight percentages of eggshell and fly ash: 4 wt% eggshell with 6 wt% fly ash, 5 wt% eggshell with 5 wt% fly ash, and 6 wt% eggshell with 4 wt% fly ash. The experimental methodology involved designing experiments with response surface methodology (RSM), considering applied load, sliding velocity, and sliding distance as variables. The outcomes showed that the composite with 6 wt% eggshell and 4 wt% fly ash exhibited superior hardness, tensile strength, and impact resistance compared to pure Al 6082 and other composite variations. Additionally, this composite reduced the coefficient of friction (COF) from 0.489 for the base Al 6082 to 0.265. ANOVA based on RSM identified sliding distance as the most influential factor affecting COF. Optimal conditions for minimizing COF were determined to be a 40 N load, 0.215 m/s sliding velocity, and 800 m sliding distance through confirmatory trials. Scanning electron microscopy (SEM) analysis of the worn surfaces provided further insights into the wear mechanisms. The research reveals that integrating the addition of eggshell and fly ash significantly enhances the mechanical properties and reduces the friction of Al 6082, with sliding distance being a critical factor in tribological performance. This investigation is distinctive due to its innovative use of dual reinforcement with fly ash and eggshell, which are abundant, cost‐effective, and rarely employed together in this context. The research thoroughly investigates various reinforcement ratios, providing vital insights into how they affect the composite's properties, particularly in terms of improving tribological properties. Using RSM and ANOVA makes the findings more precise and reliable. The investigation highlights sliding distance as the key factor affecting the tribological behavior of the composites.

Boron-doping tunes nitrogen species to abundant edge-N and B-N matrix for efficient phosphate removal in capacitive deionization: Additional accessible sites and defective centers

The elimination of eutrophication necessitates the implement of a clean and efficient phosphate removal strategy. Capacitive deionization (CDI) is recently regarded a promising approach for removing pollutants from aqueous environment with high-efficiency, facile operation, and low-energy consumption. Development of electrode materials with good properties is crucial for CDI. In this study, novel edge-N-modified boron-doped hierarchical carbon composites (MBCNs) were prepared via a hard template strategy for phosphate electrosorption. According to the pseudo-second-order kinetic, the MBCN2 electrode exhibited a dynamic capacity of 196.02 mg PO43− g−1 in 150 min with an initial concentration of 100 mg PO43− L−1 at 1.2 V. And it also demonstrated an exceptional saturation capacity of 399.70 mg PO43− g−1 under the Langmuir model. Based on theoretical analysis and experimental results, the incorporation of boron atoms in the carbon framework indicated a dual reinforcement. This included an enhanced proportion of edge-N modifications for defective centers and sufficient high-polarity B-N matrix for additional available active sites. The joint contribution of edge-N and B-N matrix revealed favorable electrical double layer capacitance, which resulted in good phosphate removal performance. This work disclosed the synergistic impact of dual nonmetallic atom doping, providing insights into the efficient phosphate removal anode for CDI.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Ultrastrong, Ductile, Tear- and Folding-Resistant Polyimide Film Doubly Reinforced by an Aminated Rigid-Rod Macromolecule and Graphene Oxide.

As a high-performance polymer with exceptional mechanical, thermal, and insulating properties, polyimide (PI) has been widely used as flexible circuit substrates for microelectronics, portable electronics, and wearable devices. Due to the growing demand for further thinning and lightweighting of electronic products, PI films need to have further enhanced mechanical properties. Traditional nanofiller-reinforced PI films often exhibit reduced ductility and limited improvements in strength. Therefore, it remains a challenge to simultaneously improve the strength and toughness of PI films while preserving their ductility. In this study, we report an exceptionally strong and ductile PI doubly reinforced by one-dimensional rigid-rod para-aramid, poly(p-aminophenylene aminoterephthalamide ((NH2)2-PPTA), and two-dimensional graphene oxide (GO) nanosheets. The amino side groups of (NH2)2-PPTA react with the anhydride end groups of PI, forming covalent bonds. At a (NH2)2-PPTA content of only 0.4 wt %, the (NH2)2-PPTA/PI film displays significantly enhanced mechanical properties. When 0.4 wt % of GO is added together with (NH2)2-PPTA, the tensile strength, tensile toughness, and strain at break reach 284.8 ± 5.3 MPa, 277.9 ± 7.6 MJ/m3, and 132.6 ± 3.8%, which are ∼178, ∼312, and ∼51% higher, respectively, than those of pure PI. Moreover, the doubly reinforced PI film also exhibits a 206% increase in tear strength and significantly enhanced folding resistance. The dual reinforcement of PI with (NH2)2-PPTA and GO improves the mechanical properties more efficiently than any single reinforcing agents previously reported and overcomes the disadvantage of most inorganic nanofillers that reduce ductility.

Dual functional composite solid electrolyte constructed by double-layer sulfonated polyethersulfone (SPES)/poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) nanofiber membrane in high-performance all-solid-state lithium metal batteries

The successful implementation of polymer electrolytes in next-generation all-solid-state lithium metal batteries (ASSLMBs) is impeded by their low ion conductivity and weak resistance to lithium dendrites. Herein, a composite solid electrolyte (D-SPES-PH-PEO) with dual reinforcement effects is obtained by combining PEO matrix with a double-layer sulfonated polyethersulfone (SPES)-poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) nanofiber membrane. The SPES nanofiber membrane acts as a hopping site for ion transport by utilizing the electronegativity and Coulombic force of its own –SO3H groups, reducing the energy barrier of the D-SPES-PH-PEO electrolyte. Profiting from the existence of C-F bond, PVDF-HFP nanofiber membrane specializes in generating a protective SEI layer on the lithium anode. Moreover, such a functional membrane not only effectually constructs a three-dimensional ion transport channel by fabricating ion conductive substructures, but also enhances the mechanical strength of the electrolyte. Consequently, the D-SPES-PH-PEO electrolyte is equipped with a high ion conductivity of 7.41 × 10−5 S cm−1 at 30 °C and high tensile strength of 8 MPa. Furthermore, Li/Li symmetric battery incorporating D-SPES-PH-PEO electrolyte enables a stable cycle for 2500 h at 0.2 mAh cm−2. The D-SPES-PH-PEO electrolyte represents outstanding compatibility with LiFePO4 and high-voltage LiNi0.8Mn0.1Co0.1O2 (NMC811) electrodes. Especially, the LiFePO4/D-SPES-PH-PEO/Li pouch cell indicates an initial specific capacity of 146.7 mAh g−1 and the NMC811/D-SPES-PH-PEO/Li pouch cell reaches a maximum capacity of 178.5 mAh g−1. This study emphasizes the importance of double reinforcement of ion conduction and anode protection on solid electrolytes.

Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production

In agriculture, irrigation is essential for providing water to crops based on the type of soil they are grown in. To achieve success in farming, it is important to evaluate soil fertility, temperature, rainfall and set irrigation schedules. In this work, to enhance agricultural production, an IoT-based hydration system that utilizes soil moisture and humidity sensors to keep an eye on soil conditions and water crops precisely is developed. This system effectively manages water usage in farming, resulting in an efficient conservation of water resources. The proposed model is categorized into four main phases: (a) Pre-processing; (b) Clustering; (c) Feature extraction; (d) Classification. Initially, the collected raw data is pre-processed via a data-cleaning approach. From the pre-processed data, DB scan is used to cluster the data. From the clustered data, the important feature is extracted by using statistical features like mean, standard deviation, kurtosis, and skewness. Subsequently, from the extracted features, the most optimal features are selected via a new hybrid meta-heuristic optimization model referred to as Cuckoo search-based Levy adolescent identity search algorithm (CLAIS). The projected CLAIS model is the conceptual amalgamation of the standard Cuckoo search optimization (CSO) and adolescent identity search algorithm (AISA), respectively. CLAIS-based deep Q network (CLAIS-DQN) classifier is used to classify the optimal features. DQN is an efficient solution to offload the request optimally which improves the overall performance of the network. The proposed model is implemented using the PYTHON platform. The proposed model has recorded the highest detection accuracy as 96%.

Influence of B4C and ZrB2 reinforcements on microstructural, mechanical and wear behaviour of AA 2014 aluminium matrix hybrid composites

Considering their affordability and high strength-to-weight ratio, lightweight aluminium alloys are the subject of intensive research aimed at improving their properties for use in the aerospace industry. This research effort aims to develop novel hybrid composites based on AA 2014 alloy through the use of liquid metallurgy stir casting to reinforce dual ceramic particles of Zirconium Diboride (ZrB2) and Boron Carbide (B4C). The weight percentage (wt%) of ZrB2 was varied (0, 5, 10, and 15), while a constant 5 wt% of B4C was maintained during this fabrication. The as-cast samples have been assessed using an Optical Microscope (OM) and a Scanning Electron Microscope (SEM) with Energy Dispersive Spectroscopy (EDS). The properties such as hardness, tensile strength, and wear characteristics of stir cast specimens were assessed to examine the impact of varying weight percentages of reinforcements in AA 2014 alloy. In particular, dry sliding wear behaviour was evaluated considering varied loads using a pin-on-disc tribotester. As the weight % of ZrB2 grew and B4C was incorporated, hybrid composites showed higher hardness, tensile strength, and wear resistance. Notably, the incorporation of a cumulative reinforcement consisting of 15 wt% ZrB2 and 5 wt% B4C resulted in a significant 31.86% increase in hardness and a 44.1% increase in tensile strength compared to AA 2014 alloy. In addition, it has been detected that wear resistance of hybrid composite pin (containing 20 wt% cumulative reinforcement) is higher than that of other stir cast wear test pins during the whole range of applied loads. Fractured surfaces of tensile specimens showed ductile fracture in the AA 2014 matrix and mixed mode for hybrid composites. Worn surfaces obtained employing higher applied load indicated abrasive wear with little plastic deformation for hybrid composites and dominant adhesive wear for matrix alloy. Hence, the superior mechanical and tribological performance of hybrid composites can be attributed to dual reinforcement particles being dispersed well and the effective transmission of load at this specific composition.

Dual-Reinforcement-Learning-Based Attack Path Prediction for 5G Industrial Cyber–Physical Systems

5G industrial cyber-physical systems have attracted substantial research interests due to their capability in the interconnection of everything. However, integrating the 5G network may expose systems to more potential risks. To reveal attack propagation, an attack path prediction approach based on dual reinforcement learning is proposed. First, a dual-network model is established, incorporating the security constraints for attacks against the 5G network into the attack graph. Second, employing reinforcement learning, Q-value updating functions and reward mechanisms based on topology and vulnerability are designed. Finally, an optimal attack path prediction algorithm is developed. Unlike traditional methods, the proposed approach does not rely on the monotonicity assumption that a system component has only one vulnerability, enabling it to accurately predict the optimal attack paths. Our simulation results demonstrate that the proposed approach can identify possible attack sources and paths from a 5G industrial cyber-physical system.

A Dual Reinforcement Learning Framework for Weakly Supervised Phrase Grounding

Weakly-supervised phrase grounding aims to localize a specific region in an image that corresponds to the given textual phrase, where the mapping between noun phrases and image regions is not available in the training stage. Previous methods typically exploit an additional proxy task (e.g., phrase reconstruction or image-phrase alignment) to provide supervision for training, since the lack of region-level annotations in the weakly-supervised setting. However, there exists a significant gap in optimization objectives between the proxy tasks and the target grounding task, which may result in low-efficient optimization for the target model. Therefore, in this paper, we propose a novel dual reinforcement learning framework to directly optimize the phrase grounding model. Specifically, we consider the duality of phrase grounding and phrase generation tasks. These two tasks form a closed loop that can provide quality feedback signals to measure the performance of each other. In this way, we can measure the correctness of the localized regions and thus be able to optimize the grounding model directly. We design two reward functions to quantify the feedback signals and train the models via reinforcement learning. In addition, to relieve the training difficulty of our framework, we present a heuristic algorithm to generate pseudo region-phrase pairs to warm-start our models. We perform experiments on two popular phrase grounding datasets: ReferItGame and Flickr30 K Entities, and the results demonstrate that our method outperforms the previous methods by a large margin.

Enhanced adhesion property of epoxy resin composites through dual reinforcement mechanisms

The study of methods to enhance the properties of composite materials for various application areas is an enduring topic. In this paper, a high-performance filler was prepared by grafting hexamethylene diisocyanate (HDI) and amino-terminated butadiene nitrile rubber (ATBN) onto graphene oxide (GO). Epoxy resin (EP) was selected as the object of feasibility verification, and the EP composite material was prepared. It is worth noting that both physical and chemical bonding mechanisms were utilized to significantly improve the adhesion properties, mechanical properties, and thermal stability of the EP composite material. The T-peel strength reached as high as 16.4 N/mm, which is a 469 % increase compared to pure EP. This paper provides a new and more efficient approach to the modification of composite materials with superior results.

Microstructure and mechanical properties of in-situ B4C reinforced TC4 composites prepared by laser melting deposition

In order to satisfy the increasingly harsh service environments of modern aircrafts, the novel in-situ reinforced xB4C/TC4 (x = 0–1.5 wt%) composites were prepared by laser melting deposition (LMD) and their microstructure, mechanical properties were investigated. The dual reinforcement phases of TiC and TiB were formed after B4C addition. A more favorable microstructure of refined α-Ti grains and uniformly-distributed reinforcement phases was obtained after adding 0.5 wt% B4C. The 0.5B4C/TC4 composite exhibited a moderate anti-abrasion ability owing to its beneficial microstructure and relatively high microhardness. Furthermore, it possessed the highest strength due to the effects of grain strengthening and dispersion strengthening induced by the (TiC + TiB) phases. This work provides theoretical basis and technical guidance for the development of high-performance Ti matrix composites.

Tribological properties of cold-sprayed 7075Al coatings reinforced with hybrid nano-TiB2/micro-SiC particles

In this study, strategies to strengthen 7075Al matrix by blending involving nano-sized TiB2 and micro-sized SiC particles were investigated in order to prepare advanced, highly wear-resistant Al matrix composites. Cold spray (CS) technology was used to deposit a TiB2/SiC-7075Al composite coating. For comparison purposes, three additional coatings with different powder compositions were produced using the same CS processing parameters. They were pure 7075Al, TiB2/7075Al composite and SiC-7075Al composite coatings, respectively. The mechanical properties, including hardness, modulus, adhesion strength and wear resistance, were examined. The powders and their corresponding coatings, as well as the wear tracks on the coating surfaces, were observed and analyzed by optical microscopy, scanning electron microscopy and energy-dispersive spectroscopy. Results showed that the in-situ formed TiB2 particles were uniformly distributed in the 7075Al matrix, while the ex-situ deposited SiC particles were mainly distributed at inter-particle boundaries. The hybrid reinforced TiB2/SiC-7075Al composite coating exhibited the highest hardness and modulus. The tribological tests showed that both TiB2 and SiC particles greatly influenced the wear behavior of the composite coatings, with the latter having a more significant effect. Dual reinforcement with TiB2 and SiC particles enabled the TiB2/SiC-7075Al composite coating to possess the best tribological properties.

Microstructure and Mechanical Performance of Tin-Based Babbitt Alloy Containing Iron Oxide and Silica Nanoparticles

Iron oxide and silica nanoparticles were individually incorporated in tin-based Babbitt alloy and combined to prepare a novel class of nanocomposites for bearing material applications. The route of liquid metallurgy in combination with the stirring technique was adopted to manufacture nanocomposites. Microstructural evolution and mechanical property evaluation were performed by optical and electron microscopy, EDS, hardness, compression, and wear tests. The morphology of the Cu6Sn5 phase was changed from elongated to spherical in the microstructures of nanocomposites. The solitary addition of 0.5 wt% iron oxide nanoparticles improved the hardness and compressive strength but adversely affected the wear properties by increasing the weight loss and friction coefficient value. In contrast, the addition of 0.5 wt% silica nanoparticles could not significantly increase the hardness and compressive strength but it could improve the tribological properties by reducing the weight loss and friction coefficient value. Tin-based Babbitt alloy showed a compressive strength of 89.22 ± 0.50 MPa after the addition of 0.5 wt% iron oxide showing a rise of ~11%. The combined effect of the addition of both types of nanoparticles showed considerable results, i.e., a rise of ~7.9% (86.75 ± 0.68 MPa). The balanced approach of incorporating dual reinforcements of 0.25 wt% iron oxide and 0.25 wt% silica nanoparticles intermediately improved the hardness, compressive strength, and decreased weight loss.

Highly Stretchable, Swelling-Resistant, Self-Healed, and Biocompatible Dual-Reinforced Double Polymer Network Hydrogels.

Superior flexibility and toughness can be achieved in bioactive hydrogels by the use of a double polymer network with complementary properties. Inspired by this design principle, we here combine polyacrylic acid (PAA) and sodium alginate (SA) to obtain a dual-reinforced double interpenetrating network (d-DIPN) hydrogel. The dual reinforcement involves ionic cross-linking and introduction of SiO2 nanoparticles, which leads to extraordinary improvements in strength and toughness. Compared with the standard PAA hydrogel that offers an elongation of 240% and a breakage stress of 0.03 MPa, the prepared SA(Ca2+)-PAA-SiO2 hydrogel shows an elongation above 1000% and a breakage stress of 1.62 MPa. Moreover, the combination of strong covalent cross-links and weak reversible interactions provides the d-DIPN hydrogel with swelling resistance and self-healing behavior, adhesive abilities, and shape memory performance. Furthermore, we show that the biocompatibility and bone cell proliferation ability of the hydrogels can be improved through a mineralization process despite an observed reduction in breakage strain and stress. Taken as a whole, our work paves the way for the design of strong and tough hydrogels, with potential applications within biomedicine and particularly tissue engineering.

Intelligent Fault Quantitative Identification for Industrial Internet of Things (IIoT) via a Novel Deep Dual Reinforcement Learning Model Accompanied With Insufficient Samples

Industrial Internet of Things (IIoT) is mainly a data-oriented network, so intelligent processing of massive data is desiderated to realize the interconnection between machines. Currently, deep-learning-based methods are widely applied for intelligent construction of the IIoT, so as to maximize the self-monitoring and self-management capabilities of various machines. However, the quantity and quality of data and the optimization of parameters greatly limit the properties of such methods. As a breakthrough of artificial intelligence (AI), deep reinforcement learning (DRL) provides inspiration and direction, which combines the advantages of deep learning and reinforcement learning to construct an end-to-end fault identification system. Therefore, a novel deep dual reinforcement learning model was proposed, which consisted of an actor model and a critic model. The dual structures avoid the over-self-optimization of the network. The action model continually learns the knowledge of identifying unknown samples by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$greedy$ </tex-math></inline-formula> algorithm, while the critic model dynamically adjusts the policy to guide the action model in right training direction. The effectiveness of the proposed method was verified by three bearing data sets. The results indicate that the proposed method enables agents to independently realize precise fault quantitative identification. The establishment of an experience storage unit overcomes the problem of insufficient samples, which avoids blind trial and error of the proposed mode.

Effect of Polyethylene Glycol and Activated Carbon Macroparticles on Thermal Conductivity of Paraffin Wax for Thermal Storage Applications.

Low thermal conductivity is the major obstacle for the wide range utilization of phase change materials (PCMs), especially organic PCMs, for most practical applications in thermal engineering. This study investigates the potential of enhancing the charging and discharging rates of organic PCM (RT44HC) by introducing polyethylene glycol (PEG) and activated carbon macroparticles (ACMPs). Different concentrations of PEG and ACMPs ranging from 0.3 wt% to 2 wt% were tested separately. The optimized concentrations found were used as dual reinforcements to attain the highest possible thermal conductivity. The specimens were tested for a complete charging–discharging cycle using an improvised thermal apparatus. Use of ACMP alone resulted in a minimal reduction in complete charging–discharging time due to the settlement of ACMPs at the bottom after 2–3 heating–cooling cycles. However, the addition of PEG with ACMPs exhibited a reduction in charging–discharging time due to the formation of a stable dispersion. PEG served as a stabilizing agent for ACMPs. The lowest charging–discharging time of 180 min was exhibited by specimens containing 1 wt% PEG and 0.5 wt% ACMPs which is 25% lower compared to bare PCM.

The Microstructural Properties and Tribological Performance of Al2O3 and TiN Nanoparticles Reinforced Ti–6Al–4V Composite Coating Deposited on AISI304 Steel by TIG Cladding

Abstract This investigation presents the tribological performance of (Al2O3 + TiN)/Ti6Al4V cladding deposited on AISI304 steel substrate by the tungsten inert gas (TIG) cladding approach. The microstructural characterization by SEM confirmed claddings with visually crack-free and sound metallurgical bonding at the clad layer—substrate interface. The energy dispersive spectroscopy (EDS) analysis revealed the presence of matrix and reinforcement phases as major elements with the clad layer and with considerably no oxidation during their deposition. The XRD spectra revealed that matrix and reinforcements are dominant phases in the clad layer. The formation of compounds reflected considerably a lower dilution of reinforcement phase with Ti6Al4V matrix during melting and deposition. Higher the microhardness of the (Al2O3 + TiN)/Ti6Al4V clad layer in the cladding zone compared with other clad layer compositions such as Ti6Al4V, Al2O3/Ti6Al4V, and TiN/Ti6Al4V, it is varied from 1130HV0.2 to 1222HV0.2, and the average microhardness is about 990.57HV0.2 which is 175% improvement compared with the substrate. The cladding with dual reinforcement composition has shown a superior wear resistance compared with all other clad layer composite compositions. The improvement in the wear resistance achieved with (Al2O3 + TiN)/Ti6Al4V composite clad layer deposition at 2.5 m/s, 3.5 m/s, and 4.5 m/s sliding velocities is 56.60%, 63.26%, and 68.53%, respectively, compared with the substrate. The wear morphology of the composite claddings is relatively smoother and the wear furrows are shallower compared with the substrate, especially for the composite clad layer with (Al2O3 + TiN) reinforcement phase.

Influence of SiC/TiB2 Particles Addition on Corrosion Behavior of As-Cast Zn-Al-Cu Alloy Hybrid Composites

In this work, the silicon carbide (SiC) and titanium diboride (TiB2) reinforced as-cast Zn-Al-Cu alloy-based hybrid composites were produced with ultrasonic-assisted stir casting. The corrosion behavior of as-cast Zn-Al-Cu/TiB2/SiC hybrid composites with varying 0, 5, 10, and 15 weight percent of SiC + TiB2 particulates in Zn-Al-Cu matrix alloy was investigated in this study, The effect of additional reinforcements content on the corrosion behavior of hybrid composites with respect to Zn-Al-Cu alloy was investigated. The corrosion rate of hybrid composites was taken by using potentiodynamic polarization equipment (with 3.5 wt.% NaCl solution). The potentiodynamic polarization test findings showed that the hybrid composites’ uniform and localized corrosion resistances were enhanced by the addition of dual reinforcements. As the weight fraction of the SiC/TiB2 reinforcement increases, weak microgalvanic cells that form between the constituent phases of the fabricated as-cast Zn-Al-Cu hybrid composites weaken, improving their resistance to uniform and localized corrosion. The results revealed that the corrosion rate values were reduced to 28.88% at 15% of reinforcement when compared to the base alloy.