Enhancing operations quality improvement through advanced data analytics (Record no. 22829)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250509160017.0 |
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| fixed length control field | 250509b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26155 |
| Author | Noman, A. H. M. |
| 245 ## - TITLE STATEMENT | |
| Title | Enhancing operations quality improvement through advanced data analytics |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.10(1), Jan-Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Ghaziabad |
| Name of publisher, distributor, etc. | MAT Journals |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 1-14p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26156 |
| Co-Author | Mustaquim, S. M. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Ghaziabad MAT Journals |
| International Standard Book Number | 2581-6969 |
| Title | Journal of computer science engineering and software testing |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://matjournals.net/engineering/index.php/JOCSES/article/view/94 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 09/05/2025 | 2025-0803 | 09/05/2025 | 09/05/2025 | Articles Abstract Database |