02021ntm a2200205 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100003500109245015300144260003400297300005700331502010800388504002800496520124200524650002701766650002201793vtls000073111KUKTEM20251114204540.0130705t2012    my da  f m    000 0 eng d  aTHE0006567(Local)  aUMP0 aMuhammad Aliff Nazreen Norazmi10aMultiple objective optimization of electrical discharge machining on titanium alloy using grey relational analysis /cMuhammad Aliff Nazreen Norazmi  aKuantan, Pahang :bUMP,c2012  axiv, 50 p. :bill. (some col.) ;c30 cm. +e1 CD-ROM  aProject paper (Bachelor of Mechanical Engineering (Manufacturing)) -- Universiti Malaysia Pahang - 2012  aBibliography : p. 40-423 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. 0aElectric metal-cutting 0aSurface roughness