Prediction of mechanical properties for polyetheretherketone composite reinforced with graphene and titanium powder using artificial neural network

Zakaulla, Mohamed; pasha, Younus; Siddalingappa, Shylesh kesarmadu

doi:10.1016/j.matpr.2021.06.365

Cited by 14 publications

(12 citation statements)

References 52 publications

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“…It is clearly found that the result obtained from ANN technique are very close as compared to the corresponding experimentally obtained values. [ 32 ] From this figure correlation factors ( R ) are 0.999, 0.969, 0.991, and 0.990 for the training subset, validation subset, testing subset and total data set, respectively. It provides excellent approximation capability, requires less time.…”

Section: Resultsmentioning

confidence: 99%

Complex catalyzed synthesis and prediction of properties of poly(acrylonitrile‐co‐acrylamide)/crab shell powder composites by using artificial neural network

Sahoo

Biswal

2023

Polymer Composites

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In a nitrogen atmosphere, a novel composite, Poly(Acrylonitrile‐co‐Acrylamide)/crab shell powder, was synthesized by combining two monomers, acrylonitrile (AN) and acrylamide (Aam), with varying weight percentages of crab shell powder (CSP) as bio‐filler. The synthesized composite Poly (AN‐co‐Aam)/CSP, copolymer Poly (AN‐co‐Aam), and CSP were characterized using Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), thermogravimetric (TGA) analysis, derivative thermogravimetric (DTG) analysis, and a scanning electron microscope (SEM). The mechanical properties, like tensile, flexural, and impact strengths, were studied. The hardness, biodegradability, and water absorbency properties have been studied using appropriate methodologies. The SEM images and FTIR data confirm the appropriate synthesis of the composite material and the uniform distribution of CSP. The mechanical strength increases with the rise in concentration of CSP up to 20 wt. % and then gradually decreases. The thermal analysis reveals that the composite exhibits adequate thermal stability. The artificial neural network (ANN) model is used for the prediction of mechanical properties, which shows adequate results with the experimental results. The training and test datasets use a correlation factor associated with this analysis that is higher than 0.9, which confirms that the expected values of the mechanical performance are an excellent comparison to the experimental value.

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Section: Resultsmentioning

confidence: 99%

Complex catalyzed synthesis and prediction of properties of poly(acrylonitrile‐co‐acrylamide)/crab shell powder composites by using artificial neural network

Sahoo

Biswal

2023

Polymer Composites

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“…The tensile strength reached a maximum at 4 wt% and a speed of 150 rpm, and these were the optimum conditions for the stress transfer from the amorphous chains of LLDPE to the graphene nanoplatelets. A similar approach was used by Zakaulla et al [101] to predict the mechanical properties of high performance polyetheretherketone (PEEK) hybrid nanocomposites comprising graphene (2-10 wt%) and titanium powder (1-5 wt%) prepared via injection moulding [101]. The proposed ANN model delivered satisfactory results to predict the hardness, tensile strength, modulus of elasticity and tensile elongation in comparison to experimental measurements (Figure 13), and the best performance was attained upon the incorporation of 10 wt% graphene.…”

Section: Property Prediction Process Optimization and Uncertainty Qua...mentioning

confidence: 99%

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Champa-Bujaico

García-Díaz

Díez‐Pascual

2022

IJMS

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Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

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“…17 In a similar way, Mohamed Zakaulla et al applied the ANN model to forecast the mechanical properties of a biocomposite material reinforced with graphene powder in the polyetheretherketone matrix. 18 In this work, we are not directly using HA, but it is extracted from oyster shell. Hence, the environmental degradation caused by the solid waste generated from oyster shells by food processing units can be prevented.…”

Section: Introductionmentioning

confidence: 99%

“…Miguel Garca‐Carrillo et al developed an ANN model for predicting the electrical conductivity and the thermal conductivity of the carbon particles of high‐density polyethylene biocomposite materials 17 . In a similar way, Mohamed Zakaulla et al applied the ANN model to forecast the mechanical properties of a biocomposite material reinforced with graphene powder in the polyetheretherketone matrix 18 . In this work, we are not directly using HA, but it is extracted from oyster shell.…”

Section: Introductionmentioning

confidence: 99%

Study of antibacterial and mechanical properties of poly(methyl methacrylate‐co‐acrylic acid)/hydroxyapatite biocomposite by using artificial neural network approach

Sahoo,

Biswal

2024

SPE Polymers

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A novel, promising biocomposite was synthesized through the free radical polymerization method using methyl methacrylate (MMA), acrylic acid (AA), and hydroxyapatite derived from oyster shells (Os‐HA). Fourier transform infrared spectroscopy (FTIR) peaks indicate the conjugation of Os‐HA in the biocomposite. From the TGA analysis, it was observed that a weight loss of nearly 2% is achieved at 210°C, up to 15% at 210 to 420°C, and up to 95% at 420 to 580°C. The SEM image shows the uniform dispersion of the Os‐HA particles and strong interaction with the copolymer matrix. The broad spectrum at 2θ = 14.6° in XRD analysis indicates the amorphous nature of poly(MMA‐co‐AA)/Os‐HA. The higher inhibition zone of the biocomposite is observed at 125 μL against E. coli bacteria. The maximum water absorption is observed at 15 wt% of Os‐HA. The young's modulus gradually increases, while the impact strength decreases up to 20 wt% of Os‐HA. The artificial neural network (ANN) model has been used to assess the optimum values of impact strength, tensile strength, and Young's modulus of the biocomposite material. The correlation factor (R = 0.998) of the ANN model data indicates greater accuracy of the experimental result.

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Prediction of mechanical properties for polyetheretherketone composite reinforced with graphene and titanium powder using artificial neural network

Cited by 14 publications

References 52 publications

Complex catalyzed synthesis and prediction of properties of poly(acrylonitrile‐co‐acrylamide)/crab shell powder composites by using artificial neural network

Complex catalyzed synthesis and prediction of properties of poly(acrylonitrile‐co‐acrylamide)/crab shell powder composites by using artificial neural network

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Study of antibacterial and mechanical properties of poly(methyl methacrylate‐co‐acrylic acid)/hydroxyapatite biocomposite by using artificial neural network approach

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