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In the present work, chemically treated kenaf fibre (10, 20 and 30 wt. %) reinforced epoxy resin composite formulations were fabricated using hand lay-up technique. The prepared composites were mechanically characterized using Taguchi L27experimental design to examine the influence of varying kenaf fibre wt. %, NaOH concentration % for chemical treatment, and fibre immersion duration. The grey-fuzzy optimization approach was utilized for optimizing prepared formulations mechanical properties such as tensile strength, impact strength and flexural strength. Further, analysis of variance (ANOVA) tests and response graphs were utilized for delivering model significance and determining the percentage contribution of input variables. The NaOH concentration % was found to be most significant parameter followed by kenaf wt. % in providing best mechanical properties of chemically treated kenaf fibre reinforced epoxy composites.

In the present study, the graphene sheet (GS) - embedded aluminium (Al) composite under compressive strain rate is analyzed using molecular dynamics (MD) simulation. The effects of pin-hole and layering of GSs on compressive strengthening behaviour of the composite material are predicted. MD simulation has been performed with the aid of open-source code LAMMPS by modelling a periodic system of GS-Al nanocomposite unit cells. Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) and Embedded atom method (EAM) potentials are employed to model the interactions between carbon atoms and the atoms of Al respectively. The simulation results show that the compressive strength of nanocomposite material is substantially larger than that of pure Al material. It is also found that the compressive elastic modulus of GS-Al nanocomposite material deteriorates because of pin-holed GS on the contrary to layered GS reinforced nanocomposites.

The present study analyzes the important characteristics of plastic injection molding machining process. The polypropylene (PP) material has used as a specimen and effect of melt temperature, packing pressure and injection pressure has been investigated on the tensile modulus and elongation. Total 20 experiments have been performed to analyses the results. Response surface methodology (RSM) was adopted for optimization of injection molding process parameters. The experiments were conducted by using central composite design. The analysis of variance (ANOVA) techniques was used for selection of significant and non-significant parameters. The experimental results show that the RSM influence elongation by 87.04%, 11.52%, 1.43% and tensile modulus by 85.35%, 11.4%, 3.25%. Keywords: Injection molding; polypropylene; tensile modulus; elongation

Development and selection of an adequate bearing material with distinct characteristics for wear resistant applications is one of the most crucial tasks. Inappropriate selection of material may cause hindrance and result in failure of components during its functioning. To this end, this research focusses on fabrication of bearing materials reinforced with varying weight percentages of industrial waste, i.e. marble dust, and the physical, mechanical and tribological characteristics were determined. The proportions of marble dust were varied from 0 wt. % to 10 wt. % with a gap of 2.5 wt. % and filled in a silicon-bronze alloy. The physical and mechanical properties were realized by the Archimedean principle, micro-hardness tester and Instron 3369 universal testing machine respectively. The tribological properties such as friction and wear behaviour were determined utilizing pin-on-disk tribometer under different condition at normal room temperature. Due to the conflicting nature of desired properties in a bearing material, the Vise Kriterijumska Optimizacija Kompromisno Resenjemeaning under fuzzy environment was applied to rank and select the optimal composite among available alternatives because it can obtain compromise solution considering overall satisfaction and regret of the selection of the wrong provinces. From the analysis of the results, it was found that silicon-bronze-3 marble dust bearing material containing 7.5 wt. % marble dust achieved the best possible set of the properties for wear resistant applications.

Fabrication and selection of optimal material with diverse properties for ship body is often a troublesome and difficult task, as inadequate selection of materials may result in improper functioning and failure of the components at any stage during its functioning. To this end, this paper presents a novel hybrid fuzzy Preference Selection Method (f-PSI) method for selection of optimal alternative ship body material based on physical, mechanical and corrosion characteristics of a novel hybrid aluminium metal alloy composites. Four hybrids aluminium alloy (AL7075) composites are fabricated through stir casting method by reinforcing varying quantity of zirconium dioxide (2%, 4% and 6%) and silicon carbide (3%, 6% and 9%). The physical characterization results show that void contents and density continuously increases with increase in the wt.% of the reinforcement. The mechanical characterization and fractography analysis results confirms upturn in hardness, ultimate tensile strength, flexural strength and impact strength up to 10% wt. of reinforcement and decreases for 15% wt. of reinforcement. The corrosion characterization and microstructural analysis show that corrosion rate decreases considerably up to 10% wt. of reinforcement and increase after that due to the precipitation of carbide particles along the grain boundary region of hybrid metal alloy composite. For validation and robustness of the proposed method, sensitivity analysis is performed, and comparison is performed with already available fuzzy TOPSIS and fuzzy VIKOR methods. Seven evaluation criteria such as void content, density, micro-hardness, ultimate tensile strength, flexural strength, impact strength and corrosion rate are considered in the study to choose the best material for ship body. From the analysis of results, it was found that A-2 (having 4 wt.% ZrO2 and 6 wt.% SiC as reinforcement) alternative material possesses the best combination of all the properties for the given ship body application.

The aim of present study is to investigate the microstructural, mechanical and tribological behavior of Zinc Alloy (ZA-27) hybrid metal matrix composites reinforced with graphite (Gr) and alumina (Al2O3). In the present study graphite and Al2O3 content is varied with composition (0, 3.5, 7 and 10.5 wt. %) prepared with liquid state stir casting process. Microstructure, density, hardness, compressive strength, impact strength and wear rate of composites have been investigated. SEM micrograph shows the uniform distribution of reinforcement particles in ZA-27 matrix. It is found that mechanical properties of composites depend on the reinforcements content. Hardness, compressive strength and impact strength of composites increases with an increase in reinforcements content up to 7 wt. %. It is observed that reinforced composites show good tribological properties. Wear resistance of composites is increased with an increase in hybrid reinforcement content. Maximum wear resistance is found at 10.5 wt.% reinforcements content. Further, TOPSIS methodology is adopted for the optimization of reinforcements content which shows that 7 wt.% of reinforcements provide better mechanical properties among all the compositions. It is expected that this composite will be beneficial in automobile industries.

In this article, the elastic and shear moduli of the graphene sheet-reinforced aluminum nanocomposite have been investigated by molecular dynamics simulations. Different models have been simulated to study the effect of multilayer graphene sheet, perforation of GS, and temperature on the elastic and shear moduli of resulting nanocomposite. The simulation results show that the elastic and shear moduli of graphene sheet-reinforced aluminum are sensitive to the temperature changes, multilayer, and perforated graphene sheets. The temperature and perforation of graphene sheets exert adverse effects on the elastic and shear moduli of graphene sheet-reinforced aluminum nanocomposites. However, the multilayer graphene sheet leads to favorable effects on the stiffness properties of the nanocomposite. It is also observed that there is only a marginal effect of the chirality of graphene sheet on the out-of-plane shear moduli of the nanocomposite.

This paper presents a comprehensive research on the influence of boron carbide and molybdenum disulphide hybrid reinforcement on physical, mechanical and tribological characteristics of copper metal matrix composite dedicated for electrical appliances application. The synthesis of novel copper metal matrix composite reinforced with boron carbide (0 to 3%) and molybdenum disulphide (0 to 4.5%) were fabricated using liquid stir casting technique. There is an uptrend in the physical and mechanical characteristics of the copper metal matrix composite with higher reinforcement addition in comparison to pure copper. The Taguchi experimental design is applied to plan the experiments and S/N ratio is used to determine optimal factor settings for minimum wear loss of the copper metal matrix composite. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) model is developed to predict the wear loss with respect to changes in input parameters viz. composition, load, sliding speed, and sliding distance. The confirmation experiments results reveal conformity between the ANFIS model and experimental results with 97.99% accuracy implying that the established ANFIS model can be precisely used for predicting the wear loss. Moreover, the novel copper metal matrix composite reinforced with boron carbide and molybdenum disulphide shows enhanced wear resistance in respect to unreinforced composite that make it appropriate for electrical appliances application.

Marine conditions are highly contentious for most materials manifested by the decayed condition of old ships and wrecks (made up of steel/wood). This work investigates the mechanical and corrosion behavior of aluminum-based composites reinforced with 3, 6, and 9 wt% of hybrid reinforcements (SiC, graphite, and ZrO2). It was observed that 3 wt% reinforcement composite had the optimum mechanical properties along with minimum corrosion rate. This composition had the least void contents, and its micro-hardness increased by 27.5% (42.6 VHN) in comparison to that of the unreinforced Al (33.3 VHN). Impact strength of the composite increased by 27.2% for 6 wt% hybrid reinforcement (247.1 J) and then started decreasing, whereas tensile strength of the composite increased by 8% for 9 wt% hybrid reinforcement (124.0 MPa) with respect to that of pure Al. The flexural strength of the pure Al reduced with the addition of hard reinforcing particles. The corrosion behavior of the composite was analyzed in 3.5% NaCl solution (simulating the seawater condition) at room temperature with the help of Tafel polarization curve and scanning electron microscopy (SEM) micrographs. It revealed that the 3% reinforced composite had the minimum corrosion current density (0.4 μA) and corrosion rate (0.23 mpy) compared to those of pure Al. The surface morphology of corrosion tested samples indicated the pitting corrosion mechanism.

This paper presents a particle swarm optimization (PSO) approach to improve the geometrical accuracy of additive manufacturing (AM) parts by minimizing geometrical dimensioning and tolerancing (GD&T) error. Four AM process parameters viz. Bed temperature, nozzle temperature, Infill, layer thickness are taken as input while circularity and flatness error in ABS part are taken as response. A mathematical model is developed for circularity and flatness error individually using regression technique in terms of process parameters as design variables. For the optimum search of the AM process parameter values, minimization of circularity and flatness are formulated as multi-objective, multi-variable optimization problem which is optimized using particle swarm optimization (PSO) algorithm and hence improving the geometrical accuracy of the ABS part.

The training process in an artificial neural network (ANN) is regarded as one of the challenging tasks in machine learning due to the complex, non-linear nature and uncertainty involved in determining optimal set of significant governing factors such as number of neurons, weights and biases. In the same context, this paper presents a novel hybridization of an improved bat algorithm (IBA) trained artificial neural network for predicting wear properties of marble dust reinforced ZA-27 alloy composites. The improvement in bat algorithm is performed by introducing a new velocity, position search equation and sugeno inertia weight, that can boost the flexibility and diversity of bat population and provides stabilize effective training of ANN model. In this research, the influence of varying wt. % (0, 2.5, 5, 7.5 and 10 %) of marble dust reinforcement on the wear performance of ZA-27 alloy composites is determined. The prediction of specific wear rate is performed using Taguchi L25 orthogonal array design and IBA-ANN methodology by performing experimental trials on pin-on- disc tribometer at five levels of each sliding velocity, filler content, normal load, sliding distance and environment temperature. The experimental and predicted values of specific wear rate shows good agreement with an overall accuracy of 96.59 % and 3-d surface plots were established for predicting specific wear rate as a function of wt. % of marble dust and other testing conditions. The results prove effectiveness of IBA trained ANN in overcoming the drawback of local optima stagnation and enhancing convergence speed and can be established as an intelligent tool for prediction of specific wear rate in marble dust reinforced ZA-27 alloy composites.