Optimization of caulogenesis in Populus nigra under lead (Pb) stress via response surface methodology (RSM) and desirability function analysis

2020 ◽  
Vol 142 (1) ◽  
pp. 41-50
Author(s):  
Ryad Amdoun ◽  
Fatiha Sahli ◽  
Kamel Hamadache ◽  
Abdel-Hakim Ouzzane ◽  
Majda Khelifi-Slaoui ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Function Analysis ◽  
Desirability Function ◽  
Populus Nigra ◽  
Pb Stress ◽  
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 Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

A Taguchi coupled desirability function analysis of wire cut EDM behaviour of titanium dioxide filled aluminium matrix composite

2020 ◽  
Vol 27 ◽  
pp. 853-858
Author(s):  
S.V. Alagarsamy ◽  
M. Ravichandran ◽  
S. Dinesh Kumar ◽  
S. Sakthivelu ◽  
M. Meignanamoorthy ◽  
...  
Keyword(s):  
Titanium Dioxide ◽  
Matrix Composite ◽  
Function Analysis ◽  
Desirability Function ◽  
Aluminium Matrix ◽  
Aluminium Matrix Composite ◽  
Desirability Function Analysis

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

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