Investigation of machining performance in ecdm of Al6061-SiC-B4C hybrid composites

This study investigates the machining performance of the Electrochemical Discharge Machining (ECDM) process on a hybrid composite material comprising Aluminum 6061 reinforced with 6% silicon carbide (SiC) and 6% boron carbide (B4C). Experiments evaluated the effects of electrolyte concentration, voltage, pulse-on time, and pulse-off time on Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR). Taguchi analysis and ANOVA highlighted the significant influence of these parameters on machining outcomes. The optimal MRR (0.113 g/min) was achieved at 20% electrolyte concentration, 40V, 150 µs pulse-on time, and 25 µs pulse-off time. The best TWR (0.007 g/min) occurred at 30% concentration, 30V, 150 µs pulse-on time, and 50 µs pulse-off time. The optimal SR (4.757 µm) was observed at 20% concentration, 30V, 100 µs pulse-on time, and 75 µs pulse-off time. The findings emphasize the importance of parameter optimization in improving machining efficiency and surface integrity, offering valuable insights for hybrid composite applications. In particular, the study reveals that higher electrolyte concentrations and voltages generally enhance MRR but can increase TWR and degrade SR. These findings underline the importance of parameter optimization for balancing productivity and surface integrity. This research provides valuable insights for industries seeking precise and efficient machining of hybrid composites, showcasing a 34% enhancement in machining efficiency and a 21% improvement in surface quality using the optimized ECDM conditions.

Authors

Institutions

Publication Details

Journal
Engineering and Technology Journal
Published
2025-03-24
DOI
https://doi.org/10.30684/etj.2025.156592.1881
Primary Topic
Aluminum Alloys Composites Properties
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigation of machining performance in ecdm of Al6061-SiC-B4C hybrid composites

Safa Lafta, Abbas Fadhil Ibrahim
4 links
Engineering and Technology Journal
Aluminum Alloys Composites Properties
article

Investigation of machining performance in ecdm of Al6061-SiC-B4C hybrid composites

Safa Lafta, Abbas Fadhil Ibrahim
article en

Abstract

This study investigates the machining performance of the Electrochemical Discharge Machining (ECDM) process on a hybrid composite material comprising Aluminum 6061 reinforced with 6% silicon carbide (SiC) and 6% boron carbide (B4C). Experiments evaluated the effects of electrolyte concentration, voltage, pulse-on time, and pulse-off time on Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR). Taguchi analysis and ANOVA highlighted the significant influence of these parameters on machining outcomes. The optimal MRR (0.113 g/min) was achieved at 20% electrolyte concentration, 40V, 150 µs pulse-on time, and 25 µs pulse-off time. The best TWR (0.007 g/min) occurred at 30% concentration, 30V, 150 µs pulse-on time, and 50 µs pulse-off time. The optimal SR (4.757 µm) was observed at 20% concentration, 30V, 100 µs pulse-on time, and 75 µs pulse-off time. The findings emphasize the importance of parameter optimization in improving machining efficiency and surface integrity, offering valuable insights for hybrid composite applications. In particular, the study reveals that higher electrolyte concentrations and voltages generally enhance MRR but can increase TWR and degrade SR. These findings underline the importance of parameter optimization for balancing productivity and surface integrity. This research provides valuable insights for industries seeking precise and efficient machining of hybrid composites, showcasing a 34% enhancement in machining efficiency and a 21% improvement in surface quality using the optimized ECDM conditions.

Engineering and Technology JournalVol. 0(0)
University of Technology - Iraq (IQ)
Openalex Percentile: Top 95%
Aluminum Alloys Composites Properties
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.