Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Surface roughness (Ra)


Influence of Machining Parameters on Surface Roughness in Chemical Machining of Silicon Carbide (SiC)

Naeem A. Abdulhusein; Abbas F. Ibrahim; Abdullah F. Huayier

Engineering and Technology Journal, 2022, Volume 40, Issue 6, Pages 879-884
DOI: 10.30684/etj.v40i6.1879

This study discussed the influence of chemical machining parameters such as (machining time, type of etchant, etching temperature, and concentration of the solution) on the surface roughness of ceramic material (silicon carbide) as a workpiece in the chemical machining (CHM) process. To achieve the best value for surface roughness. In this research, four levels of factors affecting the chemical etching process were used, the values of etching temperature (60, 80, 100, and 120) °C, the etchant concentration (50, 60, 70, and 80) %, and machining time (30, 50, 70, and 90) min, and two etchant type (HBr, HCl). Experiments proved the best value of surface roughness is obtained (2.933) µm experimentally and (2.958) µm at a predictable program when using hydrochloric acid (HCl) at a temperature (80) °C, time (50) min, and etchant concentration (50) %. The coefficient determination (R-sq) to predict the surface roughness is ((93.7).

Investigation the Effect of Negative Polarity of Surface Roughness and Metal Removal Rate During EDM Process

Shahd A. Taqi; Saad K. Shather

Engineering and Technology Journal, 2020, Volume 38, Issue 12, Pages 1852-1861
DOI: 10.30684/etj.v38i12A.1591

The Electro discharge machine that named (EDM) is used to remove the metal from the workpiece by spark erosion. The work of this machining depends on the multiple variables. One of the most influential variants of this machine is the polarity, the material of the electrode, the current and the time pulses. Essentially the polarity of the tool (electrode) positive and the work piece is negative, this polarity can be reversed in this paper was reversed the polarity that was made the tool (electrode) negative and the work piece was positive. The aim of this paper was focused on the influence of reversed the polarity (negative) with changing the electrode metal (copper and graphite) on the surface roughness and metal removal rate by using different parameters (current and pulses of time). Experiments show that: the copper electrode gives (best surface roughness 0.46 μm when the current 5 Am and Ton 5.5 μs) and (worst surface roughness 1.66 μm when the current is 8 A and Ton 25 μs). And give (best values of the MRR 0.00291 g/min when the current is 8 and Ton 25 μs) and (The lowest values of MRR (0.00054 g/min when current is 5 and Ton 5.5 μs). The graphite electrode gives (best surface roughness 2.07 μm when the current 5 Am and Ton 5.5 μs) and (worst surface roughness 4.17 μm when the current is 8 A and Ton 25 μs). And give (best values of the MRR 0.05823 g/min when the current is 8 and Ton 25 μs) and (The lowest values of MRR (0.00394 g/min when current is 5 and Ton 5.5 μs).

Influence of Polarity of Electro Discharge Machine (EDM) on Surface Roughness (SR) and Metal Removal Rate (MRR) of Low Carbon Steel

Shahd Taqi; Saad K. Shather

Engineering and Technology Journal, 2020, Volume 38, Issue 7, Pages 975-983
DOI: 10.30684/etj.v38i7A.469

Electro discharge machining (EDM) is one of a thermal process that is used for remove of metal from the workpiece by spark erosion. The work of this machine depends on multiple variables. One of the more influential variants on this machine is the change of polarity and the use of this variable is not wide and the research depends on the polarity of the machinist. Essentially, the polarity of the tool (electrode) is positive and the workpiece is negative, this polarity can be reversed. This paper focuses on the influence of changing the polarity (positive and negative) on the surface roughness and metal removal rate by using different parameters (current, voltages, polarity and Ton). Experiments show that the positive electrode gives (best surface roughness = 1.56 μm when the current = 5 Am and Ton = 5.5 μs) and (best metal removal rate = 0.0180 g/min when the current = 8 Am and Ton = 25 μs). Negative electrode gives (best surface roughness = 0.46 μm when the current = 5 Am and Ton = 5.5 μs) and (best metal removal rate = 0.00291 g/min when the current = 8 Am and Ton = 25 μs).

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

Hind H. Abdulridha; Aseel J. Helael; Ahmed A. Al-duroobi

Engineering and Technology Journal, 2020, Volume 38, Issue 6, Pages 887-895
DOI: 10.30684/etj.v38i6A.705

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.