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  DOI Prefix   10.20431


 

International Journal of Modern Studies in Mechanical Engineering
Volume 5, Issue 1, 2019, Page No: 41-47
doi:dx.doi.org/10.20431/2454-9711.0501004

Optimization of Process Parameters of Abrasive Water Jet Machining (AWJM) on H13 Hot Die Tool Steel by Grey Relational Analysis

Naidu Naresh Kumar1, G. Sharath Kumar1, M. shiva Shankar Durga1, K. Shruthi1, K. Srinivas2*

1.UG Scholars, Department of Mechanical Engineering, JB Institute of Engineering and Technology, Hyderabad, Telangana, India- 500075.
2.Asst. Professor, Department of Mechanical Engineering, JB Institute of Engineering and Technology, Hyderabad, Telangana, India- 500075.

Citation : K. Srinivas, et.al, Optimization of Process Parameters of Abrasive Water Jet Machining (AWJM) on H13 Hot Die Tool Steel by Grey Relational Analysis International Journal of Modern Studies in Mechanical Engineering 2019, 5(1) : 37-40.

Abstract

The hot work applications like extrusion tools, pressure die casting tools, forging dies and stamping dies requires high hardenabilty, excellent wear resistance, high toughness, thermal shock resistance and very high polish. H-13 hot die tool steel commonly used to satisfy these requirements. Because of its high hardness and strength H-13 hot die tool steel cannot be machined through traditional machining processes to achieve high surface finish and tight tolerances.

Abrasive Water Jet Machining (AWJM) is employed because of its tight tolerances and high surface finish and faster cut. AWJM can be used for drilling, cutting, deburring, cleaning and etching. As a part of our work, AWJM of H-13 die tool steel is considered for the study. In this work transverse speed, standoff distance and abrasive flow rate are considered as parameters and their effect on performance measures i.e Metal removal rate (MRR) and surface roughness (SR) are studied through experimental investigation.

Using grey relational analysis considered parameters are optimized for both the combination of maximum MRR and minimum Surface roughness. Grey relational analysis will be applied to generate grey relational grade (GRG) to identify the optimum process parameters. These optimum parameters can be adjusted to improve the performance of AWJM.


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