scholarly journals Estimation of Surface Roughness in Face Milling Using Response Surface Methodology and Desirability Function Analysis

2019 ◽  
Vol 3 (4) ◽  
pp. 159-163
Author(s):  
Gökhan Başar ◽  
Funda Kahraman ◽  
Ganime Tuğba Önder
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Function Analysis ◽  
Desirability Function ◽  
Face Milling ◽  
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

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.

scholarly journals Desirability Function Analysis (DFA) in Multiple Responses Optimization of Abrasive Water Jet Cutting Process

2022 ◽  
Vol 3 (1) ◽  
pp. 11-19
Author(s):  
Andrzej Perec ◽  
Keyword(s):  
Surface Roughness ◽  
Function Analysis ◽  
Desirability Function ◽  
Traverse Speed ◽  
Abrasive Water Jet ◽  
Control Parameters ◽  
Cutting Depth ◽  
Water Jet Cutting ◽  
Abrasive Water Jet Cutting ◽  
Desirability Function Analysis

This paper introduces optimization of machining parameters for high-pressure abrasive water jet cutting of Hardox 500 steel utilizing desirability function analysis (DFA). The tests were carried out according to the orthogonal matrix (Taguchi) L9. The control parameters of the process such as pressure, abrasive flow rate, and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the basis of the composite desirability value obtained from desirability function analysis and the significance of these parameters was determined by analysis of variance (ANOVA). The effects show that optimal sets for high cutting depth and small surface roughness is high pressure, middle abrasive flow rate, and small traverse speed. A confirmation test was also leaded to validate the test results. Results of the research have shown that machining efficiency at keeping good level quality of cut surface can be improved this approach.

Analysis and Optimization of Surface Roughness and Microhardness for Roller Burnishing using Response Surface Methodology and Desirability Function Approach on Aluminum 63400

2019 ◽  
Vol 18 (03) ◽  
pp. 363-378
Author(s):  
Vijay Kurkute ◽  
Sandip Chavan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Response Surface ◽  
Desirability Function ◽  
Roller Burnishing ◽  
Significant Control ◽  
Control Factors ◽  
Feed Force ◽  
Burnishing Process

In the present study, response surface methodology (RSM) has been used to optimize roller burnishing process for aluminum alloy 63400 grade. Single roller burnishing tool (carbide) is used to burnish round aluminum alloy. Experiments were performed with Box and Wilson Central Composite Design (CCD). The machining factors controlled during experimentation are speed, feed, force and number of tool passes. The response parameters are surface roughness and microhardness. The most significant control factors on the surface roughness and microhardness were determined by analysis of variance (ANOVA). A controllable process parameter is correlated with surface roughness and microhardness by mathematical model. A quadratic regression analysis is performed to compute the correlation coefficient between the experimental and predicted values. The optimum surface roughness and microhardness foreseen by the model is found to agree well with the results of the experiment. To find the optimum value of both the response, desirability approach was used. The input parameters with most desirability value are selected as the optimum solution. Hence, the most desirable burnished condition desirability value 0.872 is speed 37.9[Formula: see text]m/min, feed 0.5[Formula: see text]mm/rev, force 35.49[Formula: see text]N and number of tool passes four. Surface roughness obtained is 0.524[Formula: see text][Formula: see text] m and microhardness is 125.02[Formula: see text]HV. This is the optimum condition for minimum surface roughness and maximum microhardness. The optimum surface finish and microhardness predicted by the model are found to agree well with the results of the experiment.

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