Optimisation of Machining Parameters in Hard Turning by Desirability Function Analysis Using Response Surface Methodology

2015 ◽  
pp. 73-81 ◽  
Author(s):  
Nabil Kribes ◽  
Zahia Hessainia ◽  
Mohamed Athmane Yallese
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Hard Turning ◽  
Function Analysis ◽  
Desirability Function ◽  
Machining Parameters ◽  
Desirability Function Analysis

Application of Response Surface Methodology to Predict the Optimized Input Quantities of Parabolic Trough Concentrator

2020 ◽  
Vol 9 (3) ◽  
pp. 393-400
Author(s):  
Vijayan Gopalsamy ◽  
Ramalingam Senthil ◽  
Muthukrishnan Varatharajulu ◽  
Rajasekaran Karunakaran
Keyword(s):  
Response Surface Methodology ◽  
Numerical Model ◽  
Response Surface ◽  
Function Analysis ◽  
Desirability Function ◽  
Parabolic Trough ◽  
Global Efficiency ◽  
Energy And Exergy ◽  
Parabolic Trough Solar Collector ◽  
Desirability Function Analysis

This work carries out a numerical investigation on aluminum oxide/de-ionized water nanofluid based shield-free parabolic trough solar collector (PTSC) system to evaluate, validate, and optimize the experimental output data. A numerical model is developed using response surface methodology (RSM) for evaluation (identifying influencing parameters and its level) and single objective approach (SOA) technique of desirability function analysis (DFA) for optimization. The experimental data ensured that global efficiency was enhanced from 61.8% to 67.0% for an increased mass flow rate from 0.02 kg/s to 0.06 kg/s, respectively. The overall deviation between experimental and numerical is only 0.352%. The energy and exergy error is varied from 3.0% to 6.0%, and the uncertainty of the experiment is 3.1%. Based on the desirability function analysis, the maximum and minimum efficiencies are 49.7% and 84.9%, as per the SOA technique. This numerical model explores the way to enhance global efficiency by 26.72%.©2020. CBIORE-IJRED. All rights reserved

Optimization in Hot Turning of Nickel Based Alloy Using Desirability Function Analysis

2016 ◽  
Vol 24 ◽  
pp. 64-70 ◽  
Author(s):  
A.K. Parida ◽  
K.P. Maity
Keyword(s):  
Feed Rate ◽  
Function Analysis ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Cutting Velocity ◽  
Nickel Based Alloy ◽  
Desirability Function Analysis ◽  
Hot Turning

This paper presents a desirability function approach in order to find out an optimal combination of Machining parameters for multi-response parameters in hot turning operation of nickel based alloy. Taguchi’s L9 orthogonal array is used for experimental design. The machining parameters such as cutting velocity, feed rate, depth of cut and temperature are optimized by multi-response considerations namely power, flank wear, and MRR. ANOVA test was carried out and it was found that cutting speed is most influence parameter followed by feed rate, depth of cut and workpiece temperature. The optimization of machining parameters was found at 5.8 m/min of cutting speed, 30 °C preheating temperature, 0.2 mm depth of cut and 0.15 mm/rev feed rate

scholarly journals Optimization of multiple quality characteristics for end milling under dry cutting environment using desirability function

2021 ◽  
Vol 2070 (1) ◽  
pp. 012218
Author(s):  
V V N Sarath ◽  
N Tamiloli
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
End Milling ◽  
Function Analysis ◽  
Desirability Function ◽  
Substantial Contribution ◽  
Machining Parameters ◽  
Characteristic Analysis ◽  
Dry Cutting ◽  
Desirability Function Analysis

Abstract Milling AA6082T6 materials is a difficult venture because of their heterogeneity and a slew of problems, inclusive of surface roughness, that get up for the duration of the machining method and are connected to the material’s homes and slicing settings. The optimization of machining parameters is a crucial section inside the manufacturing method. This research introduces a unique approach for improving machining settings whilst milling aluminum alloy. A technique notorious as desirability function analysis (DFA) turned into worn to optimize machining parameters. DFA is a effective tool for optimizing multi-reaction problems. Milling research for aluminum alloy were completed using tungsten carbide end milling inserts in dry situations, based totally on Taguchi’s L9 orthogonal array. Multi-response issues, along with machining pressure and surface roughness, are used to optimize machining parameters including feed charge, spindle speed, and depth of reduce. person desirability values from the desirability characteristic analysis are used to create a composite desirability cost for the multi-responses. The most effective ranges of parameters had been discovered based at the composite desirability fee and substantial contribution of parameters has been determined the usage of analysis of variance.

Multi-Response Optimization in Turning of AA6061 T6 Using Desirability Function Analysis

2015 ◽  
Vol 812 ◽  
pp. 124-129 ◽  
Author(s):  
P. Jayaraman ◽  
L. Mahesh Kumar
Keyword(s):  
Function Analysis ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multiple Responses ◽  
Ideal Approach ◽  
Response Optimization ◽  
Desirability Function Analysis

This paper presents an ideal approach for the optimization of machining parameters on turning of AA6061 T6 aluminium alloy with multiple responses based on orthogonal array with desirability function analysis. In this study, turning parameters namely cutting speed, feed rate and depth of cut are optimized with the considerations of multiple responses such as surface roughness (Ra), roundness (Ø) and material removal rate (MRR). Multi response optimization of machining parameters was done through desirability function analysis. The optimum machining parameters have been identified by a composite desirability value obtained from desirability function analysis. The performance index and significant contribution of process parameters were determined by analysis of variance.

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