Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel. Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Signal-to-noise (S/N), the analysis of variance (ANOVA) was employed to find the optimal levels and to analyze the effect of electrochemical machining parameters on Ra and MRR. The surface roughness of the workpiece was decreased with the increase in current values and electrolyte concentration while causing an increase in material removal rate. The ability of the independent values to predict the dependent values (R2) were 87.5% and 96.3% for mean surface roughness and material removal rate, respectively.
Malaysia's growing population and industrialisation have increased solid waste accumulation in landfills, leading to a rise in leachate production. Leachate, a highly contaminated liquid from landfills, poses environmental risks and affects water quality. Conventional leachate treatments are costly and time-consuming due to the need for additional chemicals. Therefore, the Electrocoagulation process could be used as an alternative method. Electrocoagulation is an electrochemical method of treating water by eliminating impurities by applying an electric current. In the present study, the optimisation of contaminant removal was investigated using Response Surface Methodology. Three parameters were considered for optimisation: the curr
... Show MoreThe lethality of inorganic arsenic (As) and the threat it poses have made the development of efficient As detection systems a vital necessity. This research work demonstrates a sensing layer made of hydrous ferric oxide (Fe2H2O4) to detect As(III) and As(V) ions in a surface plasmon resonance system. The sensor conceptualizes on the strength of Fe2H2O4 to absorb As ions and the interaction of plasmon resonance towards the changes occurring on the sensing layer. Detection sensitivity values for As(III) and As(V) were 1.083 °·ppb−1 and 0.922 °·ppb
Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr
... Show MoreThe study aimed to identify the impact of the use of systemic approach in the collection of geographical material and cognitive motivation when fifth grade students of literary, experimental design researcher adopted a partial seizures, and telemetric to two unequal one experimental and the other officer.
The sample consisted of fifth grade literary students from secondary (inherent) for Boys in Baghdad (the Republic of Iraq. (By Mjootain, and the number of students of each group (30 students). And has rewarded the two groups, in the variables (chronological age, average scores half-year, degree IQ),
Promising researcher himself requirements of research to determine the scientific material and teaching plans and the formulation of
The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
... Show MoreThis article showcases the development and utilization of a side-polished fiber optic sensor that can identify altered refractive index levels within a glucose solution through the investigation of the surface Plasmon resonance (SPR) effect. The aim was to enhance efficiency by means of the placement of a 50 nm-thick layer of gold at the D-shape fiber sensing area. The detector was fabricated by utilizing a silica optical fiber (SOF), which underwent a cladding stripping process that resulted in three distinct lengths, followed by a polishing method to remove a portion of the fiber diameter and produce a cross-sectional D-shape. During experimentation with glucose solution, the side-polished fiber optic sensor revealed an adept detection
... Show MoreUnconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria. Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core. Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Um
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