Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Material removal rate

Optimization of MRR and Surface Roughness for 7024 AL-alloy in EDM Process

M.M. Abdulrazaq; S.K. Ghazi

Engineering and Technology Journal, 2017, Volume 35, Issue 5, Pages 546-553

Electro discharge machining is major non-traditional operations for cutting the materials due to its suitability and benefits. The experimental work of this paper deals with electrico discharge machining (EDM). A system for machining in this process has been developed. Many parameters are studied such as current, time on and time off. Different current rates are used ranging from (30, 36 and 42) Amp, found that low current gives less material removal rates and good surface roughness. The results showing that maximum MRR is achieved (0.525) mm3/min when machining current (42), time on (150), and time off (50) while good surface roughness (2.11 μm) when machining current (30), time on (50), and time off (25).The level of importance of the machining parameters for surface roughness and material removal rate is determined by using Taguchi design experiments and analysis of variance (ANOVA).

Optimization of Electrochemical Machining Process Based on Artificial Neural Network Technique

Noor Abd Al-Hassan; Shukry H.Aghdeab; Abbas F.Ibrahim

Engineering and Technology Journal, 2016, Volume 34, Issue 15, Pages 2960-2970

Electrochemical machining (ECM) is one of nonconventional machining process used to operation the most harsh materials that difficult to operate in conventional machining. This search has been used to study impact of different parameters on material removal rate (MRR) and to improve the MRR. The workpiece material in this search is stainless steel 316L, tool material from copper and NaCl (10, 25, 50) g/l was used as electrolyte. Through the experiments noted that the MRR increasing at increased current from (50 to 200) the increasing in MRR reach to 57.60%, also MRR increasing at increasing electrolyte concentration from (10 to 50) g/l increasing in MRR (reach) to 75.17 % and MRR decreasing at increased gap size from (0.5 to 1.5) mm the decreasing in MRR reach to 39.2 %. To predict the values of MRR and to get optimization, artificial neural network was used to get minimum mean squared error (MSE) and minimum average percentage error. In network, separated some values to training set and the remaining for testing set and it was noted that the predicated and experimental values are very close to each other.

The Influence of Current & Pulse off Time on Material Removal Rate and Electrode Wear Ratio of Steel 304 in EDM

Shukry Hammed Aghdeab; Vian Nihad Najm

Engineering and Technology Journal, 2015, Volume 33, Issue 8, Pages 1845-1856

EDM (Electric Discharge Machining)machine was used for machining of conducting cutting materials such as steel 304in dielectric solution (diesel fuel) by supplied byDC current values (10, 20, 30, 42 and 50A). Voltage of (140V) was used to cut (1) mm thickness.
The experimental results reveal that the material removal rate enhanced by increasing the current values also show that the Electrode wear ratio rises with increase in the current values. It is also concluded that the material removal rate reduces with increasing the pulse off time values and the electrode wear ratio.