Influence of agglomeration phenomenon in machining of AISI D2 hardened steel using nano chromium powder mixed-EDM

The Powder Mixed-Electrical Discharge Machining (PMEDM), as an influential environment, has been proven to overcome the challenges of dielectric liquids impedance, machinability, and surface integrity problems. Also, the additive powder particles played a vital role in this enhancement. Nevertheless, this environment suffers from the powder agglomeration phenomenon, as abnormal behaviour, resulting in the dropping of the performance of PMEDM. Accordingly, the present work aims to neutralize this phenomenon during machining AISI D2 hardened steel in PMEDM environment by employing 10, 15, and 20 Amps; 20, 25, and 30 μs; 2, 4, and 6 g/L for peak current (IP), pulse duration (Ton), Nano Chromium Powder (NCP) concentration (PC), respectively. These parameters, which are produced by Finite Element Analysis (FEA), predicted the boundaries, as the pilot procedure, to avoid the collapse of the removal operation. Digital Balance, SJ400 Mitutoyo, Shimadzu Vickers's tester, and SEM/EDS-Hitachi SU-1510 were invested to specify the Material Removal Rate (MRR), Tool Wear Rate (TWR), average surface roughness (Ra), Microhardness (MH), Recast Layer Thickness (RLT), and agglomeration ratio (%AG) after machining. Depending on Energy Dispersive X-Ray Spectroscopy (EDS) observations before and after machining stages, the weight percentage of chromium element reduced from 7.78% to (3.12% to 6.68%). Hence, NCP particles have not agglomerated in the interelectrode gap zone. Furthermore, the carbon and other elements agglomeration contributed in enhancing the microhardness from (655 to 675) HV to (784 to 911.65) HV. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-Analyses of Variance (ANOVA)-Response Surface Methodology (RSM), as hybrid optimization, boosted the composite desirability of Multiple-Response optimization produced by RSM from 0.6153 to 0.9997. Moreover, this hybrid technique has determined IP = 10 Amps, Ton = 30 μs, and PC = 2 g/L; IP = 20 Amps, Ton = 20 μs, and PC = 6 g/L, as the optimal and worse cases, respectively. Accordingly, the predictive cost, according to the optimal-case, has declined by 40.23% as compared to the worse case.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Universiti Tun Hussein Onn Malaysia

Year dc:date.issued

2020

Author dc:creator

• Abbas, Mohammed Abdulridha

• TJ1125-1345 Machine shops and machine shop practice

Language dc:language

en