Engineering Journal: Volume-10, Issue-8, August, 2024

Abstract— The primary nonferrous metals consist of aluminum, copper, lead, nickel, chromium, manganese, magnesium, titanium, zinc, and alloys like brass. These metals are typically extracted from minerals such as sulfides, carbonates, and silicates. The addition of alloying elements enhances their properties when added in appropriate quantities. Non-ferrous metals are widely utilized due to their favorable characteristics, such as lightweight (e.g. aluminum), high electrical conductivity (e.g. copper), non-magnetism, and corrosion resistance (e.g. zinc). One of the most significant nonferrous alloys is the Al 7178 series alloy, which is commonly used in technical applications. EDM is a method for controlled removal of metal that uses electrical discharge. This process uses an electric spark to erode the workpiece and shape the finished part. Metal removal is accomplished by applying a pulsed charge (ON/OFF) to the workpiece through an electrode using a high-frequency current. The workpiece is subjected to controlled erosion, resulting in the removal of tiny metal fragments. Typically, electrodes made of copper, graphite, and brass are utilized in EDM. The current project investigates the use of the metal matrix material Al7178 (Al2O3-reinforced Al7178) as a tool/electrode for machining the superalloy Superni 90. In this study, aluminum alloy 7178 is utilized as the base metal, Al2O3 is chosen as the reinforcement material, weight fraction 3%, 6%, 9%, 12% as the tool material, and nickel-based alloy as the workpiece being studied. Superni90 has been selected. Material removal rate, surface roughness and tool wear rate in electrical machining discharge. Regression models were developed for MRR and TWR based on experimental data. The Mitutoyo surface roughness measuring machine is utilized to measure the surface roughness of the processed workpiece. The geometric accuracy of the surfaces created on the workpiece is evaluated using MATLab software.
Keywords— Electric discharge machining, Current (I), Voltage(V), Pulse on time (Ton), Pulse off time (Toff), Surface Roughness (SR), Tool Wear Rate (TWR), Material Removal Rate (MRR), % error, Regression models Electrical Conductivity.

Abstract— This research investigates the fundamental role of symmetry properties in space and time in justifying the use of the principle of least action (PLA) to describe the creation and evolution of nanomaterials. By examining the kinetics of structural transformations in metals, we demonstrate that the PLA is a universal principle applicable to diverse physicochemical and biological processes. We explore the principle's ability to establish invariants at various structural-scale levels of metal deformation, including nano-, submicro-, micro-, meso-, and macroscale levels. Our findings highlight the significance of symmetry properties in understanding and predicting the behavior of nanomaterials.
Keywords— Symmetry properties, Space-time continuum, Principle of least action (PLA), Hamilton's principle, Invariants, Nanomaterials, Structural transformations.

Abstract— Over the years, the Internet has changed from a system essentially concerned with providing data, to a channel for communication and social cohesion (Fuchs et al.,2013). Criminals go fastidious to hide illegal activities, which is why surveillance is essential for the purpose of investigation. By carrying out surveillance, detectives can discover proof required to substantiate legal suit, or imprison a lawbreaker. This paper uses the IRAC method to explain the prevailing legal and regulatory structures in the EU and UK with respect to social media surveillance. It also gives an in-depth analysis of the rights of a person or citizen to social media privacy. It outlines the dangers of social media surveillance by authorities, and demonstrate different case laws and rulings regarding violations of citizens’ right of privacy by different authorities.
Keywords— Social media surveillance, privacy rights, EU data protection, UK data protection, IRAC method, legal and regulatory framework, surveillance and crime, digital rights.

Abstract— Electrical Discharge Machining (EDM) is one of the electrical based advanced machining method where electrical energy is used as a source to cut or remove material. Green dielectrics are utilized in machining to make the process extremely competent and secure. Hybrid aluminum 2021 tools with different copper percentages are utilized for super alloy superni 909 machining research in an effort to further lower manufacturing costs. The impact of different input parameters Investigations are conducted on the work material for material removal rate as a reaction to changes in pulse on time, pulse off time, current, voltage, and tool material. A mathematical model is created and the influence of process parameters on response is determined using the Taguchi design of experiments. Each factor's percentage contribution is calculated. The goal of this research is to provide a substitute dielectric that can be machined in an environmentally friendly and sustainable manner while producing a satisfactory output at the lowest possible tooling cost.
Keywords— EDM, material removal rate, process parameters, regression equation.

Abstract— The node-weighted Steiner tree problem is a significant issue in network design, with broad applications including telecommunications network construction, offshore oilfield development planning, and wireless ad-hoc networks. The objective of the node-weighted Steiner tree problem is to find a subtree within a given undirected graph that has the minimum total weight and includes all specified terminals. This problem is NP-hard, typically requiring complex algorithm design and exponential time. We have combined graph neural networks and deep reinforcement learning techniques to propose a new solution method. By encoding the structural information of the graph into vector form through graph neural networks and self-attention networks, and optimizing decisions using a reinforcement learning strategy network, we efficiently construct the desired node-weighted Steiner tree. To validate the model's effectiveness, we conducted extensive experiments on various types and sizes of generated graphs, covering different numbers of endpoints. The results show that in scale-free networks (BA), our algorithm performs equivalently to SCIP, with both achieving a Gap value of 0. In small-world networks (WS) and complete graphs (K), our algorithm closely matches SCIP's performance, with the highest Gap values being 0.3% and 1.76%, respectively, indicating that our algorithm can closely approximate SCIP's performance in handling these network structures., In random graphs (ER) and random regular graphs (RR), while there is a performance discrepancy between our algorithm and SCIP, the maximum gap does not exceed 5.76%. These findings demonstrate that our algorithm performs well in solving the node-weighted Steiner tree problem.
Keywords— Node-Weighted Steiner Tree; Reinforcement Learning; Graph Neural Networks; Combinatorial Optimization.