Open AccessParametric analysis of dry machining process using a novel integrated multi-attribute decision making approach
Author(s) -
G. Venkata Ajay Kumar,
A. Ramaa,
M. Shilpa
Publication year - 2022
Publication title -
decision science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 18
eISSN - 1929-5804
pISSN - 1929-5812
DOI - 10.5267/j.dsl.2021.11.001
Subject(s) - machining , topsis , selection (genetic algorithm) , machinability , ideal solution , process (computing) , parametric statistics , computer science , preference , similarity (geometry) , mathematical optimization , industrial engineering , mathematics , engineering , statistics , operations research , machine learning , mechanical engineering , artificial intelligence , physics , image (mathematics) , thermodynamics , operating system
In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.