Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer

Elsheikh, Ammar H.; Shehabeldeen, Taher A.; Zhou, Jianxin; Showaib, Ezzat A.; Elaziz, Mohamed E. Abd

doi:10.1007/s10845-020-01617-7

Cited by 128 publications

(26 citation statements)

References 48 publications

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“…As a conclusion, GF could be used to enhance the mechanical properties of PP, and rubber powder could be used to enhance the elongation of the developed composite with fewer side effects on the ultimate tensile strength. Moreover, it is recommended to apply advanced machine learning techniques, such as the artificial neural network (Elsheikh et al, 2020a(Elsheikh et al, , 2019bKhoshaim et al, 2021a), support vector machine (El-Said et al, 2021), long short-term memory network (Elsheikh et al, 2021;Saba and Elsheikh, 2020), adaptive neuro-fuzzy inference system (Elaziz et al, 2019;Shehabeldeen et al, 2019), and random vector functional link (Abd Elaziz et al, 2020;Shehabeldeen et al, 2020), to predict the mechanical properties of the developed composite.…”

Section: Tensile Testmentioning

confidence: 99%

“…Therefore, it was recommended to add different reinforcement materials to PP to form enhanced composite materials (Abdellah et al, 2018;Dewidar et al, 2010). It is well known that composite materials are a combination of two materials with different physical and chemical properties, which have been extensively investigated in the literature (Elsheikh et al, 2020c;Essa et al, 2021;Fadl et al, 2019;Liu et al, 2020;Wang and Fu, 2019;Xu et al, 2017). The glass-fiber-reinforced PP has been considered as a lightweight and cost-effective composite used in automotive and marine industries (Luo et al, 2018;Rahman et al, A. M. Elhousari et al: The effect of rubber powder additives on mechanical properties 2013; Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The effect of rubber powder additives on mechanical properties of polypropylene glass-fiber-reinforced composite

et al. 2021

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Abstract. Reinforced thermoplastic composites offer considerable advantages in structural and industrial applications in terms of high specific strength, excellent corrosion resistance, high impact toughness, high specific stiffness, recyclability, cost effectiveness, and design flexibility. Glass fibers (GFs) are the reinforcement material used in polypropylene (PP) composites to enhance PP properties with reasonable cost. To obtain better mechanical properties and decrease the overall cost, recycled rubber powder was used as a low-cost additive to PP/GF composites. The PP and GF, with different weight fractions, were mixed mechanically; then, the mixture was fed into injection molding machine. The PP/GF composites with the highest mechanical properties were selected for further improvements using rubber powder. The obtained samples were tested using tension, impact, and wear tests. The results show that tensile strength of PP/GF composite was improved by 17.8 % by adding 15 wt % GF, and there was a huge drop in impact strength by 80 % for the same GF weight fraction (15 wt %) compared to that of pure PP because of existence of GF. Wear properties and impact strength were improved by adding rubber powder to the composite, with a low weight fraction of 5 wt %, due to the shock-absorbing properties of rubber powder. Micrographs of the fractured sample surfaces revealed the good adhesion between the GF, PP, and the rubber particles.

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Section: Tensile Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of rubber powder additives on mechanical properties of polypropylene glass-fiber-reinforced composite

et al. 2021

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“…Artificial neural networks (ANN) are powerful information processing paradigms that mimic the human brain in processing data. ANNs have been employed to model different engineering problems ( Babikir et al, 2019 ; Elsheikh et al, 2020b ; Shehabeldeen et al, 2019 ). ANN has a number of advantages over other traditional modeling approaches such as handling enormous amounts of data, generalization capabilities, identifying complex relationships between dependent and independent variables, and detecting the inherent interactions between process variables ( Elaziz et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

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118

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“…Pao et al 18 proposed random vector function link network, whose most characteristic is network input weights and bias are randomly selected in a given range. The least squares algorithm is used to calculate the output weights 19,20 . Like the ELM, 21‐23 the learning speed of random vector functional link network (RVFL) is very fast and the generalization ability is good, and its application is increasingly extensive.…”

Section: Introductionmentioning

confidence: 99%

Dyed fabric illumination estimation with regularized random vector function link network

Zhou

Dexin

Guo

et al. 2020

Color Research & Application

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The unstable light source in the printing and dyeing environment would cause the change of fabric surface color and lead to serious color difference evaluation error. To solve this problem, a dyed fabric illumination estimation with regularized random vector functional link network (RRVFL) was proposed in this study. First, the Gray‐Edge framework is used to extract the color features of the sample images collected from the actual scene, and the extracted color features and the illumination information of the sample images constitute a data set. Then, considering the ill‐conditioned solution of the output weight of the traditional random vector functional link network (RVFL), regularization is proposed to solve this problem. Thus, we constitute a highly robust RRVFL dyed fabric illumination model. By analyzing the parameters that affect the precision of RRVFL model, the optimal parameters of RRVFL can be selected. Finally, the traditional RVFL, extreme learning machine (ELM), back‐propagation (BP), regularized extreme learning machine (RELM), support vector regression (SVR), and RRVFL estimation algorithm proposed in this study were compared and analyzed using the measurement standards of angle error, colorimetric error, and T‐test, respectively. The experimental results show that RRVFL has the best predictive results and the most stable performance compared with the traditional algorithm. RRVFL is 0.00036, 2.8050, 3.3518, 4.1669, and 2.9289 less than RVFL, ELM, BP, RELM, and SVR, respectively, in terms of average angle error. RRVFL is 0.0131, 0.0763, 0.0232, 0.0241, and 0.0221 lower than RVFL, ELM, BP, RELM, and SVR, respectively, in the average colorimetric error.

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Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer

Cited by 128 publications

References 48 publications

The effect of rubber powder additives on mechanical properties of polypropylene glass-fiber-reinforced composite

The effect of rubber powder additives on mechanical properties of polypropylene glass-fiber-reinforced composite

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Dyed fabric illumination estimation with regularized random vector function link network

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