Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite
Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2594974 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849307497423175680 |
|---|---|
| author | CR Mahesha R .Suprabha NPG Bhavani Prashant Sunagar Raja Ramesh P. Balamurugan Rajasekar Rajendran Anirudh Bhowmick |
| author_facet | CR Mahesha R .Suprabha NPG Bhavani Prashant Sunagar Raja Ramesh P. Balamurugan Rajasekar Rajendran Anirudh Bhowmick |
| author_sort | CR Mahesha |
| collection | DOAJ |
| description | Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (Ton), wire speed (Ws), voltage Iv, and pulse-off time (Toff) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings. |
| format | Article |
| id | doaj-art-207c214cb672411783dd5b5bab34bd46 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-207c214cb672411783dd5b5bab34bd462025-08-20T03:54:43ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/2594974Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 CompositeCR Mahesha0R .Suprabha1NPG Bhavani2Prashant Sunagar3Raja Ramesh4P. Balamurugan5Rajasekar Rajendran6Anirudh Bhowmick7Department of Industrial Engineering & ManagementDepartment of Industrial Engineering & ManagementDepartment of Electronics and Communication EngineeringCivil Engineering DepartmentDepartment of Mechanical EngineeringDepartment of MathematicsDepartment of Automobile EngineeringFaculty of Meteorology and HydrologyImproved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (Ton), wire speed (Ws), voltage Iv, and pulse-off time (Toff) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings.http://dx.doi.org/10.1155/2022/2594974 |
| spellingShingle | CR Mahesha R .Suprabha NPG Bhavani Prashant Sunagar Raja Ramesh P. Balamurugan Rajasekar Rajendran Anirudh Bhowmick Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite Advances in Materials Science and Engineering |
| title | Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite |
| title_full | Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite |
| title_fullStr | Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite |
| title_full_unstemmed | Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite |
| title_short | Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite |
| title_sort | modeling and optimization of mrr in wire electrical discharge machining of silicon particle reinforced aa6063 composite |
| url | http://dx.doi.org/10.1155/2022/2594974 |
| work_keys_str_mv | AT crmahesha modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT rsuprabha modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT npgbhavani modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT prashantsunagar modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT rajaramesh modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT pbalamurugan modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT rajasekarrajendran modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite AT anirudhbhowmick modelingandoptimizationofmrrinwireelectricaldischargemachiningofsiliconparticlereinforcedaa6063composite |