HDPE/TiO2 Nanocomposite: Fabrication and Optimization of Mechanical Property by RSM and ANN

2019 ◽  
Vol 287 ◽  
pp. 54-58
Author(s):  
Mohammad Sayem Mozumder ◽  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Mechanical Property ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Coupling Agent ◽  
Major Influence ◽  
Polymeric Nanocomposites ◽  
Artificial Neural

Polymeric nanocomposites have proven to be excellent candidate as biomaterials. However, materials and approaches used to improve the mechanical property of the polymer are still under scrutiny. In this study, improvement of mechanical property upon addition of nanotitanium oxide (n-TiO2), cellulose nanocrystal (CNC) and two different types of coupling agent was analyzed. Influence of the individual factors and its interaction with tensile strength was evaluated using analysis of variance. From the analyses of main effect and interaction effects, it could be concluded that n-TiO2and CNC have major influence on the improving mechanical properties. Moreover, the coupling agent and compatibilizing agent did not have considerable effect on the mechanical properties. The central composite design was used to evaluate the best combination of n-TiO2and CNC to be experimented. The responses were modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The predicted data was in agreement with the experimental data. The modeling accuracy and efficiency is evaluated based on regression coefficient (R square value). Both the method had recommendable R square value. However, the R square value of the Artificial neural network (R2>95%) was higher than Response surface methodology (R2>70 %).

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.

Improving the electromagnetic compatibility of electronic products by using response surface methodology and artificial neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ching-Hsiang Chen ◽  
Chien-Yi Huang ◽  
Yan-Ci Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electromagnetic Compatibility ◽  
Heat Dissipation ◽  
Optimal Parameter ◽  
Content Type ◽  
Early Stages ◽  
Artificial Neural

Purpose The purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process. Design/methodology/approach The three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network. Findings Market specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source. Originality/value The optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

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