Parametric Optimization of Abrasive Water Jet Machining of Beryllium Copper Using Taguchi Grey Relational Analysis

2018 ◽  
pp. 501-520 ◽  
Author(s):  
R. Manoj Samson ◽  
T. Geethapriyan ◽  
A. C. Arun Raj ◽  
Ashwin Ashok ◽  
Aditya Rajesh
Keyword(s):  
Grey Relational Analysis ◽  
Parametric Optimization ◽  
Water Jet ◽  
Abrasive Water Jet ◽  
Abrasive Water Jet Machining ◽  
Relational Analysis ◽  
Beryllium Copper ◽  
Grey Relational

PARAMETRIC OPTIMIZATION OF WIRE CUT EDM USING GREY RELATIONAL ANALYSIS

2020 ◽  
Vol 33 (02) ◽  
Author(s):  
Dr. N.E Arun kumar ◽  
◽  
Dr. R Sathish ◽  
M Ganesh ◽  
◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Parametric Optimization ◽  
Relational Analysis ◽  
Grey Relational

scholarly journals Material synthesis, characterization, and machining performance of stir cast beryllium copper alloy composites

2019 ◽  
Vol 43 (2) ◽  
pp. 143-152
Author(s):  
V. Sankar ◽  
R. Arravind ◽  
D. Manikandan
Keyword(s):  
Copper Alloy ◽  
Water Jet ◽  
Influential Factor ◽  
Machining Process ◽  
Machining Parameters ◽  
X Rays ◽  
Abrasive Water Jet ◽  
Abrasive Water Jet Machining ◽  
Beryllium Copper ◽  
Machining Properties

Copper alloys have important industrial roles due to their excellent properties. Beryllium copper is one of the copper-based alloys with the highest strength and excellent metal working and machining properties. Beryllium copper alloy composite is fabricated by the stir casting method. Its characteristics are studied with scanning electron microscopy and energy dispersive analysis of X-rays. The material properties of the stir cast beryllium copper alloy composites are evaluated. Hardness can be increased by the addition of silicon carbide particles. Abrasive water jet machining is an effective unconventional machining process, in which high pressure water jet and abrasive particles are used to remove material. Response parameters such as material removal rate and circularity of holes are considered. Response parameters of abrasive water jet machining are compared to electrical discharge machining. The most influential factor for the responses is determined by analysis of variance. Optimization of machining parameters is performed with the Taguchi method.

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