Open AccessApplication of hybrid Taguchi-Grey relational analysis (HTGRA) multi-optimization technique to minimize surface roughness and tool wear in turning AISI4340 steel
Author(s) -
Prashant D. Kamble,
Atul C. Waghmare,
R. D. Askhedkar,
Shilpa B. Sahare,
Brij Raj Singh
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1913/1/012142
Subject(s) - grey relational analysis , taguchi methods , orthogonal array , surface roughness , mechanical engineering , process (computing) , engineering drawing , computer science , materials science , engineering , mathematics , composite material , statistics , operating system
In this paper, an attempt is made to optimize the turning process by minimizing Surface Roughness and Tool Wear. The independent l factors used are Environmental Condition, Feed Rate, Depth of Cut, Nose Radius and Tool Types. The dependant factors are Surface Roughness and Tool Wear. Experimentations are conducted on CNC Spinner Lathe machine. AISI 4340 steel is selected as workpiece material. Three different types of Cutting tool are considered for the study. Grey Relational Analysis and Taguchi Philosophy together are used to optimize the process. As per the Taguchi method L27, Orthogonal Array (OA) is finalized for the experimentation. For the computation of the response table and ANOVA table, the Taguchi based data Analysis is used. The Variance Analysis (ANOVA) and S/N ratio (SRN) are employed to find the contribution and ranking of contribution parameters to optimize multiple output parameters.