Open AccessInfluence of machining parameters on material removal rate in chemical machining of silicon carbide (SIC).
Author(s) -
Abbas Fadhil Ibrahim,
Abdullah F. Huayier,
Naeem A. Abdulhusein
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1818/1/012217
Subject(s) - machining , surface roughness , silicon carbide , materials science , taguchi methods , ceramic , hydrofluoric acid , surface finish , hydrochloric acid , etching (microfabrication) , metallurgy , isotropic etching , composite material , layer (electronics)
This study aims to discuss the effect of chemical etching parameters such as (machining temperature, machining time, etchant type, and concentration of the solution) on the material removal rate and surface roughness of ceramic materials (silicon carbide) as a specimen in chemical machining (CHM) process. This study aims to achieve the best value for the removal rate of the material as well as to obtain the best value for the surface roughness. Four levels of chemical machining process parameters were used in this study: the values of machining time (30, 50, 70, and 90) min, the etchant concentration (50, 60, 70, and 80) % and etching temperature (60, 80, 100, and 120) °C, and two etchant type (HCl, HBr). The results showed that the Taguchi method that has been performed using MINITAB 17 software gave results with good accuracy. The best value of material removal rate is obtained (0.5102) mg/min at experimentally and (0.4816) mg/min at the predictable program, when using hydrochloric acid (HCl) at temperature (100) °C, time (30) min, and etchant concentration (70) %. The best value of surface roughness is obtained (2.933) µm at experimentally and (2.9589) µm at the predictable program, when using Hydrobromic acid (HBr) at temperature (80) °C, time (50) min, and etchant concentration (50) %. The coefficient determination (R-sq.) to predict the material removal rate (MRR) and surface roughness are (91.6) and (93.7) respectively. This means the model is perfect and fit.