Dijlah Journal of Engineering Sciences

Several machining parameters impact the process's quality. These
parameters may include variables such as the type of polarity, the kind of
workpiece, the types of dielectric materials, methods for dielectric flow, and
the selection of operational parameters, among other factors. This paper
includes optimization studies designed to provide insights into the influence
of these factors and the strategies utilized to achieve optimal performance.
The study revealed that the depth of cut consistently increased with higher
pulse time and current values. Additionally, the optimization of process
parameters for the EDM process using a copper electrode was explored.
Metal removal using electro discharge Machining (EDM) is primarily
achieved through melting. The reported experimental results indicate an
absence of melting, even during short pulses with a discharge duration of 5
seconds, despite their sensitivity to temperature variations. This is because
short pulses do not provide sufficient time for the metal to heat up
effectively (equivalent to approximately 45 minutes), leading to minimal or
no melting. In the EDM parameters used, the depth of cut increased with
higher voltage levels (140 V to 240 V), current (Ip) values (12 A, 24 A, 50
A), pulse-on time (Ton) durations (100 ms, 200 ms, 400 ms), and pulse-off
time (Toff) intervals (3 ms, 6.5 ms, 12 ms). The increase in voltage (140 V
to 240 V) and current (Ip: 12 A to 50 A) significantly contributed to the
enhanced depth of cut. The ET system utilized for these parameters also
showed a consistent increase in the depth of cut with rising voltage and
current. Testing confirmed that these parameters positively influenced the
depth of cut while simultaneously improving their combined effect. Four
parameters-voltage (140 V, 240 V), current (Ip: 12 A), and pulse rates-were
evaluated, revealing that as voltage and current increased, the depth of cut
and overall performance improved, with gradual adjustments in pulse rates
enhancing the process further.

This work is dealing to the welding technology of joining copper pipe
with aluminum pipe which are widely used in refrigeration systems
and boilers. The chemical composition analysis of both metals show
that are close to pure copper and aluminum. Experimentally this work
prove that those metals show a good weldability integrity.
A weld filler type ER4047 is used to join two pieces of pipes using
oxyacetylene welding process. This experimental work is also
involving the achievement of weld integrity and mechanical
prosperities by performing several destructive and non-destructive
examination on the weld region such as, Tensile, bending, hardness,
x-ray and Liquid penetrant). From all of the previous tests it was
confirmed that, the weld joint maintains a excellent mechanical
properties and acceptable weld defects.

The Oil Field Manager (OFM) Administration is data base designed to
introduce researchers to the structure and hierarchy of OFM projects, with a
focus on database types and configuration. Throughout the system, engaging
with the intuitive software interface through a series of hands-on exercises
that simulate practical workflows will be easier. These exercises will guide
you in establishing, modifying, and linking OFM to a database. This review
covers the basic usage of the application, helping researcher to understand
how to configure and personalize the OFM workspace and create projects. It
also provides an excellent opportunity to explore the primary functionalities
of the Oil Field Manager (OFM) software, allowing to create and customize
OFM projects effectively. In this review, unlock the essential skills to create
impactful graphs and reports while performing crucial data quality checks.
The researcher be empowered with the OFM Analysis Dashboard, a
dynamic data visualization tool designed to uncover insights for smarter
decision-making. Additionally, exploring engaging graphical displays,
including GIS map-based representations. This review presents a unique
opportunity to dive into the core functionalities of Oil Field Manager (OFM)
software, enabling you to effectively analyze and visualize reservoir and
production data in a cost-efficient and integrated way. OFM will enhance the
crew expertise in data analysis. Finally, using apps like Peloton’s
MasterView is a foolproof solution for managing well drilling and socketing
data. Master View contains tools and applications to organize, collect,
monitor, and report on data

This paper presents the confirmation and validation of satellite flight
software using environments of MATLAB. The satellite becomes a valuable
informative tool in educational field and the programs of engineering. The
aerospace company investing resource to improve and developing the
validation and verifications methodology depend on large scale aerospace
mission which tend to focus resource on spacelab developing. This work
looks at two kind of methodology in an attempt to develop the reliability of
satellite include utilizing system of software model and creation of software
requirement. This woke dose permit for basics software required to produce
the resulting specifications. Additionally, resulting graph produce for
specific satellite were high level did not find any issue, while Monterey
phoenix is efficient tools to produces study of model-based system engineer
concept.

In this paper, the advance experiment validations of cells coexistences
situation in close to real life condition is presents. Interference is a main
problem in radio communication, particularly when adaptable frequency
reuse is an inherent condition for exploiting spectral efficiencies in
heterogeneous network. A representative illustration is encounter in cellular
communication where macro cell-edge user received interferences from
minor cells transmission that use the identical radios frequencies bands.
Transforming interferences managing algorithm is employ toward this ends
because of their interdependency with many parameter of the targets
operation situations and numerous low-level implementation aspect
requirement to be prototype in real-time signals process platform to be
believably verifying. The insertion of an responsive interferences managing
schemes increases the signals process complexities at the physical layers.
This improvement was properly addressed by attractive advance equivalent
process technique, optimization of the arithmetic operation and intelligent
reprocess of logics and recollection resource in the base band processing
architectures.

Abstract- This paper presents the methodology employed for optimal
deployment of Wireless Sensor Network (WSN) IoT nodes used for wildfire
detection. The methodology focuses on utilizing the k-means clustering
algorithm for determining the most efficient positions of WSN nodes. This
chapter is organized into sections describing the problem statement, data
collection, k-means clustering algorithm, performance evaluation, and the
wildfire detection process. The primary objective of this study is to optimize
the deployment of WSN IoT nodes in a specific geographic area to improve
the accuracy and efficiency of wildfire detection. The problem involves
determining the best positions for WSN nodes so that they can effectively
monitor and report the occurrence of wildfires while minimizing energy
consumption and communication latency.

Artificial intelligence (AI) and cloud computing (CC) are two emerging
business technologies that will enable organizations to transform into
smarter businesses that will provide services to their clients in a way that
best meets their needs—that is, quickly, effectively, and affordably. In this
study, the implementation of CC and AI in the top Middle Eastern telecom
company, MGA-MENA, is examined. A Smart MGA-MENA Company's
operational services, product efficiency, and overall quality of products and
services are enhanced through the application of the twin technologies of
cloud computing and artificial intelligence.From this, it can be concluded
that cloud computing and artificial intelligence present a new and
sophisticated commercial possibility for large telecom firms like MGA
MENA, which have a wide client base and numerous transactions every
minute

In the era of artificial intelligence and technology, unmanned aerial vehicles
(UAVs) have become adaptable platforms with uses in many vital fields
such as surveillance, reconnaissance, mapping, and others such as civilian
and military environments. The UAVs are increasingly deployed across
diverse sectors, necessitating robust path planning strategies for optimal
mission execution. This report suggests integrating Artificial Intelligence
(AI) technology, enabling Unmanned Aerial Vehicles (UAVs) to be
integrated into the cloud infrastructure, thereby allowing ubiquitous access
to them. The research introduces framework architecture by enabling
efficient intelligent decision making for path-planning through complex
networking. By leveraging AI for intelligent decision-making and cloud
computing the UAVs can achieve enhanced autonomy, adaptability, and
mission success rates and the challenges face for scalable and flexible
computational resources.

This research introduces a new optimization method that combines Ant
Colony Optimization (ACO) with Lagrangian relaxation to improve the
efficiency of cloud computing offloading. The objective is to enhance the
distribution of computing activities and data transfer between mobile devices
and cloud servers in order to decrease latency and energy consumption. The
ACO method is used to effectively explore the solution space, while
Lagrangian relaxation is performed to address the equality requirements of
the optimization problem. The experimental findings confirm the efficacy of
the suggested methodology in obtaining substantial enhancements in
performance when compared to conventional methods.