Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network

Khan, Shahzad Maqsood; Malik, Samander Ali; Gull, Nafisa; Saleemi, Sidra; Islam, Atif; Butt, Muhammad Taqi Zahid

doi:10.1080/09243046.2019.1573448

Cited by 27 publications

(12 citation statements)

References 19 publications

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“…Khan et al investigated the mechanical properties of cross-ply laminated fibre-reinforced polymer composites. They developed model for the prediction of mechanical properties using ANN [ 95 ]. The composite samples were developed by altering glass fibre layers with carbon fibre layers and polyphenylene sulphide with high-density polyethylene.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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“…The flexural properties of PHPC samples were determined by using a three-point loading method as per the ASTM D790 standard [14]. A 40-ton UTM supplied by Fuel Instruments & Engineers Pvt.…”

Section: Flexural Propertiesmentioning

confidence: 99%

“…When the load is applied at three points, the flexural strength of the material can be measured [14]. Figure 8 depicts the flexural strength and the flexural modulus of PHPC samples respectively.…”

Section: Flexural Propertiesmentioning

confidence: 99%

Use of eco-benign periwinkle shell particle for making composites and study their effect on the thermal and mechanical properties of novel HDPE composites

Valsadwala

Srinivasan

Balachandran

et al. 2021

Preprint

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In this investigation, the shell powder of Littorina littorea commonly called periwinkle was used as an eco-benign filler in High-Density Polyethylene (HDPE) to form periwinkle/HDPE composites (PHPC). Understanding the effect of different particle sizes of periwinkle shell powder (PSP) and optimizing their influence on PHPC is the main scope of work. Periwinkle shell (PS) particle sizes from <53 μm to 150 μm were chosen as reinforcement. The different PSP size like <53 μm, 53 μm, 75 μm, 90 μm, 105 μm and 150 μm chosen in this study were named as PHPCL53, PHPC53, PHPC75, PHPC90, PHPC105, and PHPC150 respectively. The composites were fabricated by incorporating 1 weight % of PSP into HDPE matrix using compression molding technique and then subjected to morphological, thermal, and mechanical characterizations. Morphology studies using scanning electron microscope (SEM) confirms 150 μm PSP had best dispersion whereas 75 μm PSP resulted with agglomeration. PSP had little influence on the thermal stability of HDPE except for PHPC150 which showed rise in degradation temperature when compared to the neat sample. Mechanical properties such as hardness, Young’s modulus, impact strength, and flexural modulus were enhanced by the addition of PSP. Whereas, a decrease was noted in elongation at break (%) and flexural strength of PHPC indicating the stiffening effect of filler on HDPE. In order to understand the particle size influence better, the extension evaluation method (EEM) was performed for all samples and PHPC150 was found to be the best performing among all particle sizes.

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“…The experimental results showed that ANN gives better prediction results for dimensional change. Khan et al investigated mechanical properties of cross-ply laminated fibre-reinforced polymer composites as well as modelled and predicted the mechanical properties using ANN 29 . The composite samples were developed by altering glass fibre layers with carbon fibre layers and polyphenylene sulphide with high-density polyethylene.…”

Section: Introductionmentioning

confidence: 99%

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Amor

Noman

Petrů

2021

Sci Rep

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This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ($$p<0.1 e^{-10} $$ p < 0.1 e - 10 ) compared to MLR. The correlation coefficient of MLP model ($$>95\%$$ > 95 % ) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

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Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network

Cited by 27 publications

References 19 publications

Classification of Textile Polymer Composites: Recent Trends and Challenges

Classification of Textile Polymer Composites: Recent Trends and Challenges

Use of eco-benign periwinkle shell particle for making composites and study their effect on the thermal and mechanical properties of novel HDPE composites

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

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