000			04936ntm a2200361 i 4500
001			vtls000105504
003			KUKTEM
005			20251117113409.0
008			190225t20182018my da f am 000 0 eng d
020			_aTHE0005220(Local)
039		9	_a201905141458 _bhanafiah _y201902251030 _zsaini
040			_aUMP _beng _cUMP _erda
090			_aFKM .A45 2018 r Thesis
100	1		_aMohammed Ali, Ghusoon Ridha, _eauthor.
245	1	0	_aPrediction of fiber laser welding performance for dissimilar joints of duplex (AISI2205) and austenitic stainless steel (AISI304) / _cGhusoon Ridha Mohammed Ali
264		1	_aKuantan, Pahang : _bUMP, _c2018
264		4	_c© 2018
300			_axvi, 186 pages : _billustrations (some color), charts ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Mechanical Engineering
502			_aThesis (Doctor of Philosophy in Mechanical Engineering) -- University Malaysia Pahang – 2018
504			_aIncludes bibliographical references
520	3		_aThe 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	2	0	_aFaculty of Mechanical Engineering _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
999			_aVIRTUA40 _c7958 _d7964
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2640*2641*3000*3360*3370*3371*3380*3381*3470*5000*5020*5040*5200*6100*6500*6501*9992