Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method

• Main Author: Abbas Fadhil Ibrahim
• Format: Article
• Language: English
• Published: Al-Khwarizmi College of Engineering – University of Baghdad 2017-12-01
• Series: Al-Khawarizmi Engineering Journal
• Subjects: ECM; Carbon; MRR; Ra; Taguchi Method
• Online Access: http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/321
• ISSN: 1818-1171; 2312-0789

Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Signal-to-noise (S/N), the analysis of variance (ANOVA) was employed to find the optimal levels and to analyze the effect of electrochemical machining parameters on Ra and MRR. The surface roughness of the workpiece was decreased with the increase in current values and electrolyte concentration while causing an increase in material removal rate. The ability of the independent values to predict the dependent values (R2) were 87.5% and 96.3% for mean surface roughness and material removal rate, respectively.