Optimization of Machining Parameters in the Turning Operation of Inconel 825 Using Grey Relation Analysis

With the widespread application of Inconel alloys in manufacturing industries especially in the automobile as well as aerospace industries leads to manufacturers to pay more attention towards the understanding of machinability aspects of these alloys. Attributable to the need for large-scale manufacturing of Inconel machined components, the optimization of machining process variables become crucial to produce quality products economically by means of enhancing process performance. In common, several process parameters namely depth of cut, feed rate, and spindle speed influence the performance of turning operation in their own way. Concurrently, in the machining of Inconel alloys, the important performance indices are Material Removal Rate (MRR), surface roughness, and cutting force. This work deals with the assessment of process performance of Inconel 825 alloy amid turning operation. For the optimization of multiple responses, grey relation analysis has been employed that transforms the multiple responses into a corresponding single response known as overall grey relation index (OGI). Based on OGI, as a function of selected process variables, formulation of a non-linear regression model has been done and considered as the fitness function. To conclude, two evolutionary techniques, Teaching-Learning-Based Optimization, well famous as TLBO, and JAYA algorithm have been considered for optimization.

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Optimization of Surface Roughness During High Speed Milling of Inconel 825 Using Grey Relation Analysis

Proceedings of International Conference on Intelligent Manufacturing and Automation - Lecture Notes in Mechanical Engineering ◽

10.1007/978-981-15-4485-9_6 ◽

2020 ◽

pp. 49-59

Author(s):

Balagopal Unnikrishnan ◽

Armaan Valjee ◽

Vyankatesh Bagal ◽

Prashantkumar Patankar

Keyword(s):

Surface Roughness ◽

High Speed ◽

Grey Relation Analysis ◽

High Speed Milling ◽

Relation Analysis ◽

Grey Relation ◽

Inconel 825

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Multi-response optimisation of machining parameters in electrical discharge machining of Al LM25/AlB2 functionally graded composite using grey relation analysis

International Journal of Machining and Machinability of Materials ◽

10.1504/ijmmm.2018.10014639 ◽

2018 ◽

Vol 20 (3) ◽

pp. 193

Author(s):

N. Radhika ◽

Neeraj Punnath ◽

Sai Charan Katamreddy

Keyword(s):

Electrical Discharge ◽

Electrical Discharge Machining ◽

Functionally Graded ◽

Machining Parameters ◽

Grey Relation Analysis ◽

Functionally Graded Composite ◽

Relation Analysis ◽

Grey Relation

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Multi Response Optimization of Setting Input Variables for Getting Better Product Quality in Machining of Magnesium AM60 by Grey Relation Analysis and ANOVA

Periodica Polytechnica Mechanical Engineering ◽

10.3311/ppme.11034 ◽

2018 ◽

Cited By ~ 13

Author(s):

S. P. Sundar Singh Sivam ◽

Sathiya Moorthy Karuppaiah ◽

Bhaskar Karthik Yedida ◽

Jai Ram Atluri ◽

Shubhang Mathur

Keyword(s):

Orthogonal Array ◽

Cutting Speed ◽

Depth Of Cut ◽

Machining Parameters ◽

Cnc Machine ◽

Grey Relation Analysis ◽

Relation Analysis ◽

Tool Diameter ◽

Cutting Point ◽

Grey Relation

This study presents the optimization of Machining parameters on AM60 Mg alloy manufactured by Gravity Die Casting and with responses supported orthogonal array with Grey relation analysis. Focuses on the optimization of Machining input parameters using the technique to get minimum surface roughness, Minimum Tool Wear, Cutting Time, Power Requirement, Torque and Maximum MRR. A number of Machining trails were conducted based on the L9 orthogonal array on CNC machine. The experiments were performed on AM60 alloy using cutting tool of an ISO 460.1-1140-034A0-XM GC3 of 12,16 and 25 mm diameter with cutting point 140 degrees, throughout the experimental work under different cutting conditions. Grey relation analysis & ANOVA were used to work out the fore most important parameters Cutting speed, feed rate, Depth of Cut and Tool Diameter which affects the Response. The expected response and measured response are fairly close. The given model could be used to select the level of Machining parameters.

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