MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02644ntm a2200337 n 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125111037.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250505t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010026 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .A45 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Muhammad Amir Aminuddin, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Trend analysis on machine downtime for preventive maintenance of computer numerical control (CNC) machine / |
| Statement of responsibility, etc. |
Muhammad Amir Bin Aminuddin |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 80 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Computer Numerical Control machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. It is widely used in numerous industries including electronics. Electronic manufacturing organizations are facing an ever-increasing level of competition on a global scale making it an absolute requirement to decrease the amount of downtime that occurs during production operations to maximize machine availability and productivity. To ensure that customers' demands are met on time, downtime should be evaluated to identify the underlying causes of the issue and mitigate its effects. The study aims to analyze and forecast the trend of Computer Numerical Control (CNC) machine downtime through predictive modelling using machine learning models. In this study, eXtreme Gradient Boosting and Random Forest have been used to forecast the trend of future downtime occurrences. Based on the results of comparative performance analysis, the study reveals that eXtreme Gradient Boosting model outperforms Random Forest in forecasting future CNC downtime, demonstrating lower prediction errors. The outcome of this research is an interactive dashboard that integrates the analysis of historical and forecasted CNC machine downtime trends. This effort aims to provide valuable support to the industry by enhancing their preventive maintenance. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |