The present experimental research explores the integration of ternary nano-enhanced materials into an organic phase change material (PCM), using Erythritol as the base PCM. Three distinct nano-enhanced phase change materials are synthesized, incorporating different nanoparticles and multi-walled carbon nanotubes. The thermal performance of mono, binary, and ternary nano-enhanced PCMs based thermal energy storage systems is compared to the base PCM. A parabolic dish solar collector-based thermal energy storage system is fabricated as an indigenous experimental setup and tested for central Indian climatic conditions. The incorporation of ternary nanocomposite nano-enhanced PCM demonstrates the highest thermal conductivity and attains a maximum temperature of 183 °C during field testing. During charging, it exhibits all three phases (solid, solid–liquid, liquid), indicating its strong energy storage potential compared to the conventional thermal energy storage system. The ternary nano-enhanced PCMs-based thermal energy storage system stores 35

Because of the exceptional thermal characteristics, phase change materials (PCMs) are served in a variety of solar collector designs to increase the amount of useable heat obtained by collectors while minimising waste heat. Furthermore, the addition of nanoparticles to PCMs to form nanocomposite (NC) PCMs with enhanced thermal characteristics has opened the door to new possibilities for the use of nanoparticle dispersed phase change materials (NPCMs) in solar collectors. This review of the literature focuses on the effect of incorporating various types of nanoparticles into PCMs on the heat transfer behaviour and thermal conductivity (TC) of materials. Based on the findings of the previous investigations, it can be concluded that, despite the fact that silver nanoparticles are vulnerable to oxide formation, silver nanoparticles are an efficient alternative for raising the TC of PCM. Moreover, metal oxide nanoparticles outperforms than other metal-based nanoparticles in terms of raising the thermal conductivity of PCMs when compared from without any additions.

Because of the widespread use of spray-painting in the automotive industry, the automated spray-painting method has recently sparked attention in industry and research. The benefits of automating the spray-painting process include enhanced quality, efficiency, less labour, a cleaner environment, and, most importantly, affordability. The statistical tool Taguchi's DOE method is used in this study to evaluate the performance characteristics of an industrial robot Fanuc 250ib for an automated painting operation. The Taguchi method with L25 orthogonal array is utilized for the creation of the experiment, which takes includes 3 input parameters and 5 different levels for each input parameters. The goal of this research is to explore and optimise the primary controlling factors for enhanced paint coating quality as evaluated by Dry Film Thickness, leads to lower refusal. This is achieved using response surface methodology (RSM) metamodeling to optimized the spray-painting parameters used by Fanuc Paint Robot P-250iB/15. This research work helps to develop the mathematical correlations of input and response parameters to achieve the optimized paint work through Robot.

In brass utensil manufacturing, most of work is performed manually and repetitive in nature by unskilled workers with help of different tools and semi-automatic machine. The method adopted for flow forming operation is not scientific approach. Entire operations are falling under the class of man machine systems. This paper investigates most influenced input variables like work piece parameter, tooling parameter, process parameter, machine parameter and extraneous factor are directly involved with response variables like accuracy, surface finishing and cycle time has measured which decides quality, performance of flow forming operation in brass utensil manufacturing. These identified factors are used to design the mathematical model for this operation by applying theories of experimentation. The same models are simulated for collected data by statistical tool.

Presently, natural fibres are often used as reinforcement in polymer composites so as to improve the strength of polymer composites. Natural fibre besides being eco-friendly and abundantly available also reduces the consumption of expensive polymer resin. Excellent tensile strength and young's modulus makes it a worthy reinforcement to polymer composites. Thus, present review deals with the mechanical properties of ramie reinforced polymer composites. Impact strength, flexural strength and tensile strength of ramie composites are discussed. This study also provides an insight to the factors influencing mechanical properties of composites like treatments, fibre loading, polymer matrix and type of reinforcements. It also elucidates the future prospects on ramie fibre reinforced polymer composites.

In the underground coal mine the Roof is supported by bolt so that coal can be easily extracted from the mine. Presented Research work is to formulate the mathematical model and optimization of productivity of roof bolting operation. The various parameters like dependent and independent has been identified. In this research work dependent and independent pi (pi) terms are formed. The correlation of various parameters and pie terms is a mathematical model. From the studies on the model of productivity it has been observed that the influence of specification of bolt and humidity is predominant over the specification of drill rod, operator, and illumination.2D graphical analysis of the mathematical models shown the influence of the operator on productivity is significant. The optimum values of the independent pi (pi) terms can be found by optimization of these models for maximum productivity. Optimum values of independent pi terms for maximum productivity are found to be Pi 1 = 8.398, Pi 2 = 1.548 e -08, H 3 = 1.77829eH,4331=.1311, Pi H = 5457.088 and Pi 6 Pi 96.

Aluminium metal matrix composite (Al MMC) is most promising sector due to its extra ordinary properties of great strength to weight ratio, low specific gravity, improved corrosion resistance, optimum wear resistance and low thermal expansion. Aluminium matrix composite is mainly used in automobile and aviation and now a day's used in construction site for making aluminium frames. Al MMC has two phases, one phase is parent aluminium phase makes impregnate structure network called matrix and second phase is reinforcement implanted in Aluminium matrix to meet the product requirement. Reinforcement usually is hard and stiff ceramic materials (Al2O3, SiC, B4C) and heavy metals (W, Mo, Ti, Pb) which distributed the load of Al matrix and enhanced the mechanical and tribological properties of metal matrix composite materials. Characteristics of Aluminium metal composite can be tailored based on nature and weight proportion of reinforcement.

Belt conveyor is a complex electro mechanical system. The belt is the most costly element in the system. Belt failure happens due to excessive stretch occurs during starting. Sudden rise in transient tension, results in belt failure and structural damage. It is difficult to measure these transient stresses. Belt elongation is an evident quantity for these stresses. In this research work, a convex horizontal conveyor system has been considered as series of lumped mass parameter. By Lagrange's approach, equation of motion has been developed. Simulation technique (Simulink (R)) has been used to evaluate these equations for dynamic quantities. Dynamic analysis of transient stretch occurs in Polyester - Nylon (EP) fabric belt is investigated for fully loaded starting condition. Maximum dynamic stretch developed in transient condition is less than 2% of the total belt length which comprises standard value.

A binary logistic regression (BLR) model was developed to predict the performance of engineering students in university examination. This model shows the mathematical relationship between influencing factors and the performance of engineering students in university examination. Pass/fail result in university examination is taken as performance measure, and personal, pre-admission, institutional and self-learning factors as influencing factors. This BLR model was validated by Artificial Neural Network (ANN) and tested by using newly collected samples. The accuracy of the model was found as 80.95 % which showed a good accuracy for such type of models. The optimum values of each influencing factors were also calculated as 4.01, 3.16, 4.12 and 4.04 for personal, pre-admission, institutional and self-learning factors respectively. In sensitivity analysis, it is observed that personal factors are most sensitive followed by institutional factors whereas pre-admission factors are less sensitive than self-learning factors. This study will help the engineering students to improve their performance in university examination by predicting their probability of passing in advance.