Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish Input–Output Relationships of a Process

2018 ◽  
pp. 299-318
Author(s):  
Amit Kumar Das ◽  
Debasish Das ◽  
Dilip Kumar Pratihar
Keyword(s):  
Regression Analysis ◽  
Multi Objective Optimization ◽  
Input Output ◽  
Multi Objective

Input–Output Modeling and Multi-objective Optimization of Weld Attributes in EBW

2021 ◽  
Vol 46 (4) ◽  
pp. 4087-4101
Author(s):  
Amit Kumar Das ◽  
Debasish Das ◽  
Sanjib Jaypuria ◽  
Dilip Kumar Pratihar ◽  
Gour Gopal Roy
Keyword(s):  
Multi Objective Optimization ◽  
Input Output ◽  
Multi Objective

scholarly journals Metaheuristic Approach of Multi-Objective Optimization during EDM Process

2018 ◽  
Vol 3 (3) ◽  
pp. 301-314 ◽  
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Regression Analysis ◽  
Removal Rate ◽  
Test Results ◽  
Multi Objective Optimization ◽  
Machining Processes ◽  
Multi Objective ◽  
Contour Plots ◽  
The Individual ◽  
Pulse On Time ◽  
Edm Process

In modern-day manufacturing Electric Discharge Machining (EDM) process has successfully placed itself in the domain of precision machining and generating complex geometries where secondary machining processes are eliminated. In this research paper, a die sinking EDM is applied to machine mild steel in order to measure the different multi-objective results like Material Removal Rate (MRR) and Over Cut (OC). This contradictory objective is accomplished by using the control parameters like a pulse on time, duty factor, gap current and spark gap employing copper tool with lateral flushing. Here the individual objective function of the responses is created through regression analysis. Primarily the contradictory objectives are optimized by employing Taguchi Methodology, then Regression analysis is done on the test results. Additionally, the experimental results are optimized using Response Surface Methodology (RSM). It is followed by a multi-objective optimization through Overlaid contour plots and Desirability functions to ascertain the best parametric combination amongst the set of feasible alternatives.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective
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