MARC details
| 000 -LEADER |
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| fixed length control field |
04936ntm a2200361 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000105504 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113409.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190225t20182018my da f am 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0005220(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905141458 |
| Level of effort used to assign nonsubject heading access points |
hanafiah |
| -- |
201902251030 |
| -- |
saini |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 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) |
FKM .A45 2018 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohammed Ali, Ghusoon Ridha, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Prediction of fiber laser welding performance for dissimilar joints of duplex (AISI2205) and austenitic stainless steel (AISI304) / |
| Statement of responsibility, etc. |
Ghusoon Ridha Mohammed Ali |
| 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 |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
© 2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 186 pages : |
| Other physical details |
illustrations (some color), charts ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Mechanical Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy in Mechanical Engineering) -- University Malaysia Pahang – 2018 |
| 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 demand for dissimilar metals joints in recent times has increased, due to their environmental friendliness, energy conserving, efficient performances, and costeffectiveness aspects. Duplex (AISI2205) and austenitic (AISI304) stainless steel are mainly in high demand due to their ability to enhance component properties and weight reduction. However, the studies on the joining of these alloys are limited despite their potential application advantages in the marine, nuclear, and petrochemical industries. Additionally, the process of producing good dissimilar welded metals has been a major problem due to the different chemical and mechanical properties of the metals to be joined under a conventional welding condition. Furthermore, studies on the influence of both cooling rate and temperature gradient on the weld metal microstructure are of equal interest and beneficial since the properties of the final weld are related to the resultant weld metal microstructure. In this research, the main goal is modeling the fiber laser welding process of dissimilar materials of industry importance through a design of experiment (DoE) approach. A type-K thermocouple was used to measure the temperature distribution of the weld metal. The data collection process involved the controlling of the selected welding process parameters (peak power-PP (1.2-1.6) kW, welding speed-WS (1.5-2.1) mm/s, pulse width-PW (4-6) ms, and pulse repetition rate- PRR (10-20) Hz) to relate the responses (penetration depth (PD), hardness (HV), and ultimate tensile strength (UTS) of the produced joints. The experimental data were collected by characterization of the welded samples for metallographic, hardness behavior, and tensile strength. The samples were later analyzed through optical and scanning electron microscopy (SEM) equipped with energy dispersive x-ray spectroscopy (EDXS) to inspect the defect (porosity and cracks), weld bead profile, and fracture mode. The contribution of each parameter, as well as their major interactive effects on the model output was determined through analysis of variance (ANOVA) and graphically presented. Moreover, an artificial intelligence system was developed for the prediction of the laser welding process using artificial neural network (ANN). The results showed that pulse mode fiber laser welding technique was successfully used to butt-weld two dissimilar metals (AISI304 and AISI2205). Duplex steel has the higher cooling rate compared to austenitic steel due to the higher rate of heat transfer and thermal conductivity of duplex compared to that of austenitic stainless steel. An optimized process condition comprised of PP 1.2 kW, WS 1.5679 mm/s, PW 5.999 ms, and PRR 10 Hz yielded the optimum values of PD, 1.989 mm; HV, 415.519; and the highest UTS value of 654.4 MPa. The obtained values of PD, HV, and UTS from the experimental validation process were compared to those predicted by the mathematical model and a percentage error of 2.34 % was observed, showing the accuracy of the model. Hence, the developed mathematical model sufficiently matched the experimental and validation results. The accuracy of the ANN prediction network was evaluated by root mean squared (RMS) error between the measured and the estimated values with percentage error of 0.205 %. The ANN model achieved more accurate results compared to the RSM model in terms of (RMS, R2, and Mean error %) for the three responses PD, HV, and UTS. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Mechanical Engineering |
| 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 |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |