scholarly journals OPTIMIZATION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK IN GEARED DRILLING

2021 ◽  
Vol 13 (2) ◽  
pp. 116-123
Author(s):  
Yegireddi Shireesha ◽  
◽  
Nandipati Govind ◽  
◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Filler Material ◽  
Drill Bit ◽  
Ann Model ◽  
Drilling Operation ◽  
Artificial Neural

Drill on different layered composite causes to force the layers ahead of it, creating unacceptable delamination on the leaving side. The current work describes the influence of different process parameters like spindle speed, feed, diameter of drill bit and addition of filler material on delamination damage on carbon and jute hybrid fiber composites during drilling operation. The experimental output for delamination is optimized by RSM-Response Surface Methodology and ANN-Artificial Neural Network Model. The experimental prediction model was established by considering DOE (design of experiments) of three levels performed with drilling operation by varying above said process parameters The optimum values for minimized delamination damage conditions found to be at (J-C-C-J) +5g of filler (fly-ash) composite. This is drilled with 8.5mm diameter drill bit with a feed of 0.08mm/rev at 875rpm speed. From the theoretical results it is recognized that cutting-speed and filler-material have much influencing factors on responses (delamination), and their individual contribution in an order of 47.25% and 47.32% respectively. By using Box-Behnken design RSM model is developed, with a feed-forward back-propagation method to develop the predictive ANN model which consists of 15 neurons in its hidden layer along with ANN Model. Here ANN Results (R2=0.99and RMSE=1.99) showed that the developed model is performing better to predict content of delamination when compare to RSM results (R2=0.97and RMSE=2.24).

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

scholarly journals OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY

2020 ◽  
Vol 26 (2) ◽  
pp. 77-84
Author(s):  
SYLVESTER UWADIAE ◽  
FAITH OVIESU ◽  
BAMIDELE AYODELE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Box Behnken Design ◽  
Garcinia Kola ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Solvent Volume

The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. The optimized process values were 397.86 mL and 399.99 mL for solvent volume; 109.32 min and 107.55 min for extraction time; 72.64 g and 70 g for sample mass and maximum yields of 20.839 wt% and 20.488 wt% for RSM and ANN respectively. The highly positively correlated experimental and anticipated values validated the models.

Estimation of mechanical properties of friction stir processed Al 6061/Al2O3-Tib2 hybrid metal matrix composite layer via artificial neural network and response surface methodology

2021 ◽  
pp. 146442072110345
Author(s):  
Vahid M Khojastehnezhad ◽  
Hamed H Pourasl ◽  
Arian Bahrami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Friction Stir Processing ◽  
Wear Behavior ◽  
Friction Stir ◽  
Al 6061 ◽  
Artificial Neural

Friction stir processing is one of the solid-state processes which can be used to modify the structure and properties of alloys. In addition, it has become one of the most promising techniques for the preparation of the surface layer composites. To pursue cost savings and a time-efficient design, the mathematical model and optimization of the process can represent a valid choice for engineers. Friction stir processing was employed to generate an Al 6061/Al2O3-TiB2 hybrid composite layer, and mechanical properties such as the hardness and wear behavior were also measured. The relationship between the hardness and wear behavior, process parameters of friction stir processing were evaluated using an artificial neural network and response surface methodology. The rotational speed (1500–1800 rpm), traverse speeds (25, 50, 100 mm/min), and the number of passes (1–4) with constant axial force (2.61 kN) were used as the input, while the hardness and weight loss values were the output. Experimentally, the results showed that the process parameters have significant effect on hardness and wear behavior of Al 6061/Al2O3-TiB2. In addition, the developed artificial neural network and response surface methodology models can be employed as alternative methods to compute the hardness and weight loss for given process parameters. The results of both models showed that the estimated values for the hardness and wear behavior of the processed zone had an error less than 0.60%, which indicated reliability, and an evaluation of the estimated values of both models and the experimental values confirmed that the artificial neural network is a better model than response surface methodology.

scholarly journals Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction

2021 ◽  
Vol 343 ◽  
pp. 03012
Author(s):  
Iliass El Mrabti ◽  
Abdelhamid Touache ◽  
Abdelhadi El Hakimi ◽  
Abderahim Chamat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Goodness Of Fit ◽  
Ann Model ◽  
Goodness Of Fit Test ◽  
Artificial Neural ◽  
Input Variables ◽  
Springback Prediction

In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.

Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO(OH)-NWs–AC using response surface methodology and an artificial neural network

RSC Advances ◽  
2016 ◽  
Vol 6 (24) ◽  
pp. 19768-19779 ◽  
Author(s):  
Farshid Nasiri Azad ◽  
Mehrorang Ghaedi ◽  
Arash Asfaram ◽  
Arsalan Jamshidi ◽  
Ghasem Hassani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Activated Carbon ◽  
Response Surface ◽  
Process Parameters ◽  
Rhodamine B ◽  
Simultaneous Removal ◽  
Ferric Oxyhydroxide ◽  
Artificial Neural

The present study deals with the simultaneous removal of chrysoidine G (CG), rhodamine B (RB) and disulfine blue (DB) by Ni doped ferric oxyhydroxide FeO(OH) nanowires on activated carbon (Ni doped FeO(OH)-NWs–AC).

scholarly journals USE OF RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK APPROACH FOR METHYLENE BLUE REMOVAL BY ADSORPTION ONTO WATER HYACINTH

2020 ◽  
Vol 4 (2) ◽  
pp. 83-89
Author(s):  
Rajnikant Prasad ◽  
Kunwar D. Yadav
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Water Hyacinth ◽  
Color Removal ◽  
Ann Model ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The Individual

The release of coloured effluents from various dying industries are of great concern due to the challenge involved in the treatment process. In present work, response surface methodology (RSM) and artificial neural network (ANN) were used to predict the color removal using adsorption process. Water hyacinth (WH) was used as an economical adsorbent for color removal from aqueous solution in a batch system. The individual effect of influential parameter viz. initial pH, MB (dye) concentration, and the adsorbent dose were studied using the central composite design of RSM. The RSM result was used as an input data along with final pH (non-controllable parameter) after adsorption to train the ANN model. Color removal of 96.649% was obtained experimentally at the optimized condition. A comparison between the experimental data and model results shows a high correlation coefficient (R2RSM = 0.99 and R2ANN = 0.98) and showed that the two models predicted MB removal indicating WH can be used as an adsorbent for color removal from dye wastewater.

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