Prediction of storage and loss modulus in dynamic mechanical analysis using adaptive neuro-fuzzy interference system and artificial neural network

2010 ◽  
Vol 6 (2) ◽  
pp. 207
Author(s):  
S. Bose ◽  
D. Shome ◽  
C.K. Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Mechanical Analysis ◽  
Loss Modulus ◽  
Mechanical Analysis ◽  
Dynamic Mechanical ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

scholarly journals Experimental and artificial neural network approach for prediction of dynamic mechanical behavior of sisal/glass hybrid composites

2021 ◽  
pp. 096739112110378
Author(s):  
Heitor Luiz Ornaghi ◽  
Francisco M Monticeli ◽  
Roberta Motta Neves ◽  
Ademir José Zattera ◽  
Sandro Campos Amico
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Behavior ◽  
Storage Modulus ◽  
Loss Modulus ◽  
Mathematical Tool ◽  
Dynamic Mechanical ◽  
Dynamic Mechanical Behavior ◽  
Artificial Neural ◽  
Tan Δ

The dynamic mechanical behavior (storage modulus, loss modulus, and tan δ) of hybrid sisal/glass composites was investigated in the temperature range of 30–210 °C, for two different volume percentages of reinforcement along with the different ratios of sisal and glass fibers. Based on the experimental outcome, an artificial neural network (ANN) approach was used to predict the dynamic mechanical properties followed by a surface response methodology (SRM). The ANN analysis showed an excellent fit with the storage modulus, loss modulus, and tan δ experimental data. In addition, the fitted curves with the ANN approach were used to propose equations based on SRM. The simulation result has shown that the ANN is a potential mathematical tool for the structure–property correlation for polymer composites and may help researchers in the development and application of their data, reducing the need for long experimental campaigns.

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

Active control of surge in centrifugal compressors using a brain emotional learning-based intelligent controller

2015 ◽  
Vol 230 (16) ◽  
pp. 2828-2839 ◽  
Author(s):  
Ali Jokar ◽  
Roozbeh Zomorodian ◽  
Mohammad Bagher Ghofrani ◽  
Pooya Khodaparast
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Fidelity Simulation ◽  
Variable Speed ◽  
Load Line ◽  
Neuro Fuzzy ◽  
Performance Map ◽  
Compressor Performance ◽  
Artificial Neural ◽  
Brain Emotional Learning

Efforts have been targeted at providing a comprehensive simulation of a centrifugal compressor undergoing surge. In the simulation process, an artificial neural network was utilized to produce an all-inclusive performance map encompassing those speeds not available in the provided curves. Two positive scenarios for the shaft speed, constant, and variable, were undertaken, and effects of load line on the dynamic response of the compressor have been studied. In order to achieve high-fidelity simulation in the variable speed case, an artificial neural network was utilized to produce an all-inclusive performance map encompassing those speeds not available in the provided curves. Moreover, effects of dynamic characteristics of throttle valve were also investigated. A novel controlling scheme, based on neuro-fuzzy control philosophy, was implemented to stabilize the compressor performance in the unstable region. Results indicate that if applied, this scheme could produce practical and satisfactory outcomes, possessing certain virtues compared to available techniques.

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