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Operational Advancement Through Data-Driven Machine Learning Techniques

by Joyeshree Biswas, Md Masum Billah, Amit Deb Nath, Numair Bin Sharif, Iqtiar Md Siddique
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 15
Year of Publication: 2024
Authors: Joyeshree Biswas, Md Masum Billah, Amit Deb Nath, Numair Bin Sharif, Iqtiar Md Siddique
10.5120/ijca2024923527

Joyeshree Biswas, Md Masum Billah, Amit Deb Nath, Numair Bin Sharif, Iqtiar Md Siddique . Operational Advancement Through Data-Driven Machine Learning Techniques. International Journal of Computer Applications. 186, 15 ( Apr 2024), 45-51. DOI=10.5120/ijca2024923527

@article{ 10.5120/ijca2024923527,
author = { Joyeshree Biswas, Md Masum Billah, Amit Deb Nath, Numair Bin Sharif, Iqtiar Md Siddique },
title = { Operational Advancement Through Data-Driven Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2024 },
volume = { 186 },
number = { 15 },
month = { Apr },
year = { 2024 },
issn = { 0975-8887 },
pages = { 45-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number15/operational-advancement-through-data-driven-machine-learning-techniques/ },
doi = { 10.5120/ijca2024923527 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-27T03:06:39.075740+05:30
%A Joyeshree Biswas
%A Md Masum Billah
%A Amit Deb Nath
%A Numair Bin Sharif
%A Iqtiar Md Siddique
%T Operational Advancement Through Data-Driven Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 15
%P 45-51
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The layer-wise production paradigm of additive manufacturing technologies allows for the collecting of a huge number of pieces. This study focuses on the application of data analytics algorithms for real-time monitoring in additive manufacturing processes. The utilization of advanced analytics plays a pivotal role in enhancing the quality control and efficiency of these manufacturing techniques. The research explores how data-driven insights can be harnessed to identify, analyze, and rectify deviations in the manufacturing process, ensuring optimal performance and product quality. By integrating sophisticated monitoring algorithms, the study aims to create a robust framework that continuously analyzes various parameters during additive manufacturing. This includes monitoring factors such as temperature, pressure, and material properties in real-time. The collected data is processed through advanced analytics tools to detect anomalies or deviations from the expected standards. The implementation of machine learning algorithms further facilitates predictive maintenance and proactive adjustments, contributing to the overall reliability and effectiveness of additive manufacturing processes. The outcomes of this research hold significant implications for industries relying on additive manufacturing technologies, providing a foundation for improved process control and product quality. The study contributes to the growing field of Industry 4.0 by showcasing the integration of data analytics as a key enabler for efficient and reliable additive manufacturing.

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Index Terms

Computer Science
Information Sciences

Keywords

Additive manufacturing Correlation Linear Regression Multiple Logistic Regression Decision Tree.

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