The influence of electrochemical machining parameters on power consumption and MRR

The electrochemical machining (ECM) process is a highly developed method of working with metal. This process can be used to machine objects that are difficult or impossible to create using conventional machining processes. In this context, monitoring power consumption in industrial companies makes the process more energy-efficient, cost-effective, and sustainable, lowers energy waste from machine tools, and, thus, reduces costs. Therefore, the primary objective of this research is to determine how the ECM impacts power consumption and the material removal rate (MRR). Specifically, the workpiece was made of aluminum 6061 alloys, Al-Sic, and Al-B4C, using a stir casting process, while the tool was made of copper. The experiment is conducted by varying the input process parameters, including the electrolyte content (10,20,30) g/l, the voltage (10, 20, and 30) V, the gap (0.2, 0.5, and 0.8) mm, and the type of material. The Taguchi design of the experiment used orthogonal arrays, and the results were analyzed using the Minitab software. Four input variables, with three-factor level values for each variable, were examined in this experiment. The results showed that power consumption was decreased at a low voltage of 10 V to 104.6W, and at a high voltage of 30 V, the MRR increased to 0.074 (mm3/min). Furthermore, the most important parameters for power are the voltage and the material, followed by the gap and then the concentration. At the same time, voltage and concentration are the most important variables for the MRR, followed by concentration and material.

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Publication Details

Journal
Engineering and Technology Journal
Published
2025-04-06
DOI
https://doi.org/10.30684/etj.2025.155315.1854
Citations
1
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.73
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article

The influence of electrochemical machining parameters on power consumption and MRR

Abbas Fadhil Ibrahim, lara A. Salman, Baraa M.H. Albaghdadi
1 citations
Engineering and Technology Journal
Advanced Machining and Optimization Techniques
0.73
article

The influence of electrochemical machining parameters on power consumption and MRR

Abbas Fadhil Ibrahim, lara A. Salman, Baraa M.H. Albaghdadi
article en
1 citations

Abstract

The electrochemical machining (ECM) process is a highly developed method of working with metal. This process can be used to machine objects that are difficult or impossible to create using conventional machining processes. In this context, monitoring power consumption in industrial companies makes the process more energy-efficient, cost-effective, and sustainable, lowers energy waste from machine tools, and, thus, reduces costs. Therefore, the primary objective of this research is to determine how the ECM impacts power consumption and the material removal rate (MRR). Specifically, the workpiece was made of aluminum 6061 alloys, Al-Sic, and Al-B4C, using a stir casting process, while the tool was made of copper. The experiment is conducted by varying the input process parameters, including the electrolyte content (10,20,30) g/l, the voltage (10, 20, and 30) V, the gap (0.2, 0.5, and 0.8) mm, and the type of material. The Taguchi design of the experiment used orthogonal arrays, and the results were analyzed using the Minitab software. Four input variables, with three-factor level values for each variable, were examined in this experiment. The results showed that power consumption was decreased at a low voltage of 10 V to 104.6W, and at a high voltage of 30 V, the MRR increased to 0.074 (mm3/min). Furthermore, the most important parameters for power are the voltage and the material, followed by the gap and then the concentration. At the same time, voltage and concentration are the most important variables for the MRR, followed by concentration and material.

Engineering and Technology JournalVol. 0(0)
University of Technology - Iraq (IQ)
Affordable and clean energy
Openalex Percentile: Top 30%
Advanced Machining and Optimization Techniques
0.73
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