Search

The dissimilar combination of 2.25Cr-1Mo (P22) and modified 9Cr-1Mo (P91) obtained using Gas Tungsten Arc Welding (GTAW) process employing the Ni-based superalloy filler ERNiCr-3 (IN82) and ERNiCrMo-3 (IN625) have been investigated for microstructure evolution and mechanical properties. The butt weld joint was produced using single bevel groove geometry. The structural integrity of the welded joint was measured in respect of tensile strength, impact toughness and hardness. The alloying elements’ segregation at the inter-dendritic areas of the weld metal was witnessed while using the IN82 and IN625 filler. The impact test trials showed the mixed mode of fracture with an impact toughness of 82 ± 6 J and 70 ± 5 J for IN82 and IN625 filler, respectively, ensuring that the welded joint was safe for the end boiler application. The tensile test coupons were fractured from the P22 base metal in all the trials and for both the fillers which confirmed the negligible effect of the filler composition on the tensile properties. The hardness plots showed the inhomogeneity in hardness value, which was also supported by the microstructure evolution along the weldments. The average hardness of the IN82 filler was measured lower than the IN625 filler.

The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

Vague logic plays an important role in the reliability analysis of a repairable standby system consisting of N + 1 units. The present paper also investigates the reliability of an N + 1 units system in which at starting stage all units of the system are in good condition but when in the system the failure comes to some module, the system immediately takes reconfiguration operation, within negligible time. One repairman is available to repair one faulty module at any time, except when the system is fully failed. The functioning of the repaired unit is performed successfully, and the repair rate is constant from state one to another state.

Today, we live in the age of information. And we all know, where there is information, there is data. And currently, the amount of information present is humongous. Worse, every day the amount of information present is increasing. As a direct consequence of that, there is an enormous amount of data that is being generated on a day to day basis and the majority of that data is generally noise data (meaningless data). The data that is useful to us is quite less and is hidden in between all the useless data. Finding the useful data is like finding a pin in the haystack. This is where Data Mining and its techniques come to play. The data mining is the process of searching and then segmenting the data by discovering useful and interesting patterns as well as descriptive and predictive models from large scale data [1]. The objective of this paper is to review, analyze and then have the application of the different techniques which are used in Data mining and the discovery of knowledge in databases.

The electronic component model inventory is a method of balancing investments to achieve the service-level goal. Here you can see the electronic components warehouse and the distribution centers for the electronic components inventory. Environmental heritage policy, electronic components, and electronic component warehouses are very important issues, as the supply chain of electronic components for chemicals is directly linked to people’s lives. Variable electronic component order quantity, economical electronic component order quantity, electronic component time order quantity, electronic component removal order quantity, and electronic environment component order quantity inventory rules are usually used in the modern trend for inventory management of parts of electronics. However, effectively managing the inventory of a substance in electronic components is a difficult problem due to its properties. We have proposed the PSO inventory policy for resellers of electronic component warehouses in the electronic supply chain. We also model a model designed to measure the effectiveness of administrative strategies. The proposed algorithm for artificial bee colony (PSO) determines the optimal product at the time of the order using the current stock. The simulation results show that the ABC algorithm for artificial bee colonies is an effective way to manage electronic component warehouses and electronic component supply chains.