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Abrasive Water Jet Machining of Al5083/ Zro2/ B4c Hybrid Aluminium Metal Matrix Composite and Optimization of Its Process Parameters


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1 Sri Venkateswara University College of Engineering, Tirupati, India
     

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In this work, Abrasive water jet machining experiments are conducted on Al 5083/ B4C/ ZrO2 metal matrix composites. Experiments are conducted according to Taguchi’s experimental design (OA27) for different combinations of nozzle diameter, stand-off distance, jet pressure, abrasive flow rate, and traverse speed. The experimental data of material removal rate and surface roughness are recorded for these runs and are analysed using Taguchi - Genetic algorithm method for identification of optimal process parameters. Further, ANOVA is conducted to determine the contribution of each of these parameters on machining responses. This work is more useful for maximizing MRR thereby the machining process will be done much faster and at the same time, minimizing the Surface Roughness so as to obtain a smoother finish. The confirmation test is done at optimal parameters combinations and results are satisfactory.

Keywords

Machining, Material Removal Rate, Surface Roughness, Taguchi- Genetic Algorithm, Optimization of Process Parameters.
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  • Abrasive Water Jet Machining of Al5083/ Zro2/ B4c Hybrid Aluminium Metal Matrix Composite and Optimization of Its Process Parameters

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Authors

K. Vijayalakshmi
Sri Venkateswara University College of Engineering, Tirupati, India
G. Bhanodaya Reddy
Sri Venkateswara University College of Engineering, Tirupati, India

Abstract


In this work, Abrasive water jet machining experiments are conducted on Al 5083/ B4C/ ZrO2 metal matrix composites. Experiments are conducted according to Taguchi’s experimental design (OA27) for different combinations of nozzle diameter, stand-off distance, jet pressure, abrasive flow rate, and traverse speed. The experimental data of material removal rate and surface roughness are recorded for these runs and are analysed using Taguchi - Genetic algorithm method for identification of optimal process parameters. Further, ANOVA is conducted to determine the contribution of each of these parameters on machining responses. This work is more useful for maximizing MRR thereby the machining process will be done much faster and at the same time, minimizing the Surface Roughness so as to obtain a smoother finish. The confirmation test is done at optimal parameters combinations and results are satisfactory.

Keywords


Machining, Material Removal Rate, Surface Roughness, Taguchi- Genetic Algorithm, Optimization of Process Parameters.

References