Advances in Science and Technology Vol. 106

Abstract: The behavior of a composite material under mechanical loading condition is significantly influenced by the geometrical dimensions (length and diameter) and the total content of reinforcing fiber. Therefore, this research work focused the effect of fiber length and content on the mechanical behavior of pineapple leaf fiber (PALF) reinforced-epoxy composites. In this regard, the total of four composite samples for each fiber length (10, 15, 20, and 25 mm) and content (17, 23, 34, and 43 vol.%) were developed using a hand lay-up molding technique and characterized for mechanical properties according to ASTM standards. The tensile and flexural strength of a composite was increased with the increase of PALF length and content up to 15 mm and 34 vol.% respectively. However, the composite of 25 mm fiber length with 43% fiber volume content exhibits the maximum impact strength.

Abstract: Aluminum (Al) - Silicon Carbide (SiC) metal matrix composite is one of the widely used composites in today’s manufacturing industry. Al-SiC composites are produced through several methods such as casting and powder metallurgy, but its production through Reverse Two-Step Sintering (R-TSS) process in powder metallurgy has not been addressed so far. The present work focuses on manufacturing Al-SiC metal matrix composite through reverse two-step sintering process in powder metallurgy. The reinforcement element SiC is embedded with metal matrix element Al in different proportions. Then the consolidated mixture is compacted using the die and punch setup followed by a two-step sintering process suggested by Wong; thereby, the final compact is produced. Further, the processed sample is analyzed for density and hardness tests.

Abstract: Aluminium Matrix Composites (AMCs) are known as lightweight and high-strength materials with potential applications in areas such as aerospace, automobile, defence, engineering, and other industries. AMCs have the advantage of significantly reduce the overall weight of the vehicles and aircraft while maintaining their structural strength. The scope of this work is to fabricate Silicon Carbide (SiC) particle Metal Matrix Composites (MMC) by stir casting combined with mechanical stirring and to investigate the effect of SiC particles on the hardness, tensile and impact the behaviour of SiC particle reinforced 7075 aluminium alloy composites. The reinforcement of micron-sized range particles with an aluminium matrix is expected to improve the mechanical properties in composite materials. Different weight % of SiC particles are used (0, 5, 10, and 15 wt. %) for the synthesis of composites. The manufactured composites were tested to determine their mechanical properties and the results prove that the sample with 10 percentage of silicon carbide has better mechanical properties, comparably.

Abstract: Thermal Barrier Coating are highly advanced material systems usually applied to metallic surfaces, such as gas turbine or aero-engine parts, operating at elevated temperatures. They have ceramic and metallic multilayers which have been widely used in the aeroturbine engines to increase the life of metallic components and turbine efficiency. Many different types of coatings are used to protect variety of engineering materials from wear, corrosion and erosion. Of all these, TBC’s play a vital role in providing thermal insulation and protect the material from high temperature environment. In this paper Lanthanum Zirconate (La₂Zr₂O₇) is used as a coating material which is phase-stable to its melting point, Lanthanum Zirconate is a promising material which exhibit lower thermal conductivity and higher thermal stability compared to other TBC system. High quality Lanthanum Zirconate based TBC is developed by plasma spray technique on superalloys. In the present investigation porosity, microstructure, hardness and bond strength of the developed TBC’s were characterized and significant parameters of plasma spray process were identified.

Abstract: This paper presents the salient details of a simulation-based study conducted to analyze the effect of due date assignment methods and scheduling decision rules on the performance of a flexible manufacturing system. A typical FMS is considered for investigation in the present study. Three endogenous due date setting methods and one exogenous method are examined in the present study. The scheduling rules considered for experimentation include processing time based rules and due date based rules in addition to the unbiased first-in-first-out rule. The performance measures evaluated in the present study are mean flow time, standard deviation of flow time, mean tardiness, standard deviation of tardiness, percentage of tardy jobs and average flow allowance. Analysis of the simulation results reveal that the dynamic due date setting methods provide better system performance.

Abstract: The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and tool geometry on the surface finish values. Research showed that soft computing fuzzy logic model developed produces smaller error and has satisfactory results as compared to response regression model during machining.

Abstract: Thermal energy storage (TES) based on hidden heat concept is good substitute for sensible heat storage because of its dense storage capacity and almost constant temperature heat transfers during the charging and discharging cycle. During no load and low cooling load conditions the system stores the thermal energy in the storage medium (phase change material) which will be used latter to meet the requirement in off cycle conditions. The intention of present work is to increase the system off cycle time, maintain required temperatures during power cuts by joining a few inch thick layer of phase change material on the outer surface of the evaporator. For investigation purpose a deep freezer which runs on vapor compression system of 50 liters storage capacity is fabricated with and without phase change materials. The eutectic compositions nearly 23 wt% salt (NaCl) dissolved in water and aluminium nitrate around 26 wt% dissolved in water are used as phase change materials. By the end of all experimental investigations it was noticed that the off cycle time system with phase change material is increased by 5.5 hours compared to system without phase change material, food storage time is enhanced by 8 to 14 hrs and a little power saving also achieved.

Abstract: Wind energy is the quickest growing sustainable energy resource in present energy crisis scenario. It has been considered as one of the most viable sources of environmental friendly energy. Starting investment cost of the wind turbine plant is exorbitant. Moreover, production cost of the wind turbine blade is about 20% of the wind turbine plant cost. It is fundamental to decrease the life-cycle cost of wind turbine plant by efficient utilization available wind speed. Optimized diffuser (Convergent divergent type and Convergent type) has been developed with highest possible pressure difference between inlet and exit of shroud, Area Ratio of inlet to exit section, wall length, incident angle and various flow qualities to enhance the available wind velocity considerably. The suitable tiny riblets on external layer of turbine blade have been introduced to lessen the skin friction drag force. Moreover, dual rotor blade with various rotor sizes for primary and secondary rotor, direction of rotor rotation, separation distance between them has been studied to augment wind turbine power generation and improvement in cut-in-speed. Moreover, comparative study will be conducted with standard (bare) wind turbine. Based on the above features, available wind speed increased significantly. In addition, various experiments and CFD analysis work still to be done to assess Diffuser based Wind Turbine model which is much closer to realistic product with available interaction. Due to the above additional features of the turbine system, the utilization of wind speed gets augmented with greater power production.

Abstract: Damping properties are crucial in determining the dynamic structural response. In this paper, the experimental results for Neoprene rubber of 40, 50 and 60 shore A hardness are reported in view of improving structural damping to control noise and vibrations. Additionally, the system loss factors of the unconstrained layer damped structures of same material were predicted by Ross-Kerwin-Ungar equation to validate the obtained experimental results. The results showed that Neoprene rubber (also known as Polychloroprene) of 60 shore A showed better static and dynamic characteristics than those of the 40 and 50 shore A hardness. The system loss factor results reached the saturation when the applied viscoelastic layer thickness was increased from 40 mm to 50 mm in unconstrained damping. As such, the proposed method can help to build a database of the properties of various materials which are applicable in the design of noise and vibration control.

Abstract: Controlled cooling rate is essential in steel production in order to obtain the desired grades for specific mechanical properties. Optimal control of cooling process parameters is important to obtain the desired cooling rate. The system level uncertainty around the cooling process, the model form error around the generative model for the cooling process as well as the measurement noise make the problem of optimal cooling even more challenging. Machine learning approaches have been used in the recent past to solve optimization and optimal control problems. The present study sets out to design an optimal and robust cooling rate controller using a data-driven approach within a machine learning framework which accounts for the uncertainties inherent in the system. A Gaussian process regression model is developed to predict the cooling rate using temperate-time data and two simulated latent parameters with a suitable confidence interval. The experiments have been undertaken on a laboratory scale Run Out Table setup. The results show the suitability of the proposed approach to obtain a robust response surface of the cooling rate with the process parameters.