MARC details
| 000 -LEADER |
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| fixed length control field |
04599ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111041.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250519t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Qualifying information |
hardback |
| International Standard Book Number |
THE0010083 (Local) |
| 040 ## - CATALOGING SOURCE |
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| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| Original cataloging agency |
UMPSA |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKMA .S93 2024 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Syafiq Ahmad Abdul Aleem, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Predictive monitoring of laser beam welding with enhanced denoising methods using acoustic and optical signals / |
| Statement of responsibility, etc. |
Syafiq Ahmad Bin Abdul Aleem |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 133 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Mechanical and Automotive Engineering Technology |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
The increased demand for predictive monitoring in Laser Beam Welding (LBW) process for quality management has led to the development of systems utilising various signals with the combination of acoustic and optical signals being particularly cost-effective and less complex. However, further study is needed to explore the combination of both approaches, as the effect of signal noise and selectable features on the classification model remains unclear, with limited findings supporting the effectiveness of this combination. This project aims to develop a classification model to estimate the weld penetration conditions of LBW through the analysis of signals using the proposed method for denoising and feature extraction. To achieve the aims of this research, an experiment was conducted using Boron Steel (22MnB5) with two different thicknesses, 1.2 mm and 1.8 mm. The experiment involved manipulating laser power (280 – 320 W) and weld speed (1.0 – 1.5 mm/s) to obtain several penetration conditions, namely, full penetration, half penetration, incomplete penetration and overheat penetration. The acoustic signal and optical spectrum were acquired during the process. The study found that the acoustic average amplitude and optical spectrum intensity are linearly proportional to the penetration depth. Due to process instability, a significant amount of spatters appeared, exhibiting non-stationary random noise in both signals, which led to a high Misclassification Rate (MCR) for both signal features. To overcome the issue, EMDbased Denoising with Z-score threshold and Spectrum Averaging was implemented. The result showed that out of the 15 features analysed, 12 showed improvement after denoising, with the highest improvement recorded to be 68.17%. The feature selection analysis identified Intensity and Log Different Absolute Standard Deviation (LDASD) as the most significant features in the original signal, while Intensity and Absolute Value of Summation of Square Root (AVSSR) were identified as the most significant features in the denoised signals. When comparing the original model to the denoised version, the average accuracy improved from 97.4% to 99.8% in the training set and from 95.5% to 99.0% in the test set. Additionally, the precision improved by 53.3% in the training set and 11.17% for the test set, indicating greater consistency and reliability in the denoised model. This research successfully enhanced the denoising method for acoustic and optical signals which improved the effectiveness of signal features for prediction models with notable feature enhancements such as Intensity (68.17%), RMS (31.51%), and AVSSR (26.98%). By developing classification models using both original and denoised signals, the research achieved performance gains with the denoised model reaching 99.8% accuracy in the training set and significant improvements in precision which underscore the critical role of signal preprocessing in optimizing machine learning models for the LBW process. This research is expected to advance the LBW process by providing a predictive monitoring system that enhances quality and efficiency with potential benefits for defect detection across various industries, ultimately leading to cost savings and improved product quality. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Mechanical and Automotive Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |