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003 KUKTEM
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008 130705t2012 my da f m 000 0 eng d
020 _aTHE0006567(Local)
039 9 _a201905161009
_bamirul
_y201307051205
_zhuda
040 _aUMP
090 _aTJ1191 .A45 2012 rs Bc.
100 0 _aMuhammad Aliff Nazreen Norazmi
245 1 0 _aMultiple objective optimization of electrical discharge machining on titanium alloy using grey relational analysis /
_cMuhammad Aliff Nazreen Norazmi
260 _aKuantan, Pahang :
_bUMP,
_c2012
300 _axiv, 50 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Mechanical Engineering (Manufacturing)) -- Universiti Malaysia Pahang - 2012
504 _aBibliography : p. 40-42
520 3 _aThis report deals with the machining workpiece Titanium Alloy using electrical discharge machining (EDM). The objective of this thesis is to optimize the surface roughness (SR), electrode wear ratio (EWR) and material removal rate (MRR) by using grey relational analysis (GRA) with orthogonal array (OA) and to discuss on the significant result by using Analysis of Variance (ANOVA). The machining of Titanium Alloy workpiece was performed using the EDM machine AQ55L (ATC) and the analysis done using equation for GRA and STATISTICA software for ANOVA. In this study, the machining parameters, namely workpiece polarity, pulse off time, pulse on time, peak current and servo voltage are optimized. A grey relational grade obtained from the grey relational analysis is used to solve the EDM process with the multiple performance characteristics. Optimal machining parameters can then be determined by the grey relational grade as the performance index. Based from the result, the most significant parameter that affects the MRR, EWR and SR was the peak current while significant parameter was pulse off time. Experimental results have shown that machining performance in the EDM process can be improved effectively through this approach.
650 0 _aElectric metal-cutting
650 0 _aSurface roughness
999 _aVIRTUA40
_c3928
_d3934
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992