Prediction of Swelling Behaviour of Jute and Banana Fiber Composites by using ANN and Regression Analysis

Pujari, Satish; Ramakrishna, A.; Padal, K. T. Balaram

doi:10.1016/j.matpr.2017.07.201

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Moreover, Pujari et al . (2017 b ) predicted the volume changes of jute and banana fibre composites by using ANN and regression analysis and they indicated that ANN performs better than regression analysis.…”

Section: Resultsmentioning

confidence: 99%

Growth and yield estimation of banana through mathematical modelling: a systematic review

et al. 2022

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Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model ‘SIMBA’ and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models.

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Section: Resultsmentioning

confidence: 99%

Growth and yield estimation of banana through mathematical modelling: a systematic review

et al. 2022

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“…Red mud, when added to glass-epoxy and bamboo-epoxy, results in increase in mechanical properties as well as wear resistance of hybrid composites [16]. Researchers have also made an attempt to optimise the physical phenomenon of swelling of jute-epoxy and banana-epoxy composites by using ANN (artificial neural network) and regression analysis [17]. In this study, it was observed that ANN was able to predict the swelling phenomenon better than regression analysis.…”

Section: Introductionmentioning

confidence: 91%

A fuzzy logic approach for modelling and prediction of mechanical properties of hybrid abaca-reinforced polymer composite

Sinha

Narang

Bhattacharya

2020

J Braz. Soc. Mech. Sci. Eng.

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At present, hybrid natural fibre-reinforced polymer composites are popularly used for their remarkable specific strength. Natural fibre polymer composites have been explored by the researchers for their sprawling use in engineering applications. To achieve better mechanical properties, it is needed to test hybrid natural fibre composites with all possible combinations of their compositions which require a lot of resources. Thus, the present work deals with the investigation of mechanical properties of hybrid abaca-epoxy composites. Experiments were carried out according to full factorial design with three input parameters namely weight per cent of abaca fibre, particle size of red mud and weight per cent of red mud. Subsequent to this, a fuzzy model is developed to predict the mechanical properties such as tensile, flexural and impact strength of hybrid abaca-epoxy composites based on the experimental results obtained by their mechanical characterisation. Membership functions were constructed such that the fuzzy model can precisely predict the mechanical properties of hybrid composites. Moreover, a set of test case experiments were conducted so as to validate the fuzzy model. It was inferred from these test case results that the developed model can be used to predict mechanical properties of hybrid composites with a maximum accuracy of 87%.

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“…The moisture absorption of composites containing natural fibres has several adverse effects on their properties; hence, it can cause an increase in moisture (leading the fibres to swell), a decrease in their mechanical properties, provide necessary conditions for biodegradation, and change their dimensions [ 82 ]. Natural fibre reinforced composites have low durability and inherently absorb a lot of moisture.…”

Section: Physical Properties Of Interply Hybrid Compositesmentioning

confidence: 99%

Physical, Mechanical and Perforation Resistance of Natural-Synthetic Fiber Interply Laminate Hybrid Composites

et al. 2022

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Natural and synthetic fibres have emerged in high demand due to their excellent properties. Natural fibres have good mechanical properties and are less expensive, making them a viable substitute for synthetic fibers. Owing to certain drawbacks such as their inconsistent quality and hydrophilic nature, researchers focused on incorporating these two fibres as an alternative to improve the limitations of the single fibre. This review focused on the interply hybridisation of natural and synthetic fibres into composites. Natural fibres and their classifications are discussed. The physical and mechanical properties of these hybrid composites have also been included. A full discussion of the mechanical properties of natural/synthetic fibre hybrid composites such as tensile, flexural, impact, and perforation resistance, as well as their failure modes, is highlighted. Furthermore, the applications and future directions of hybrid composites have been described in details.

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Prediction of Swelling Behaviour of Jute and Banana Fiber Composites by using ANN and Regression Analysis

Cited by 16 publications

References 16 publications

Growth and yield estimation of banana through mathematical modelling: a systematic review

Growth and yield estimation of banana through mathematical modelling: a systematic review

A fuzzy logic approach for modelling and prediction of mechanical properties of hybrid abaca-reinforced polymer composite

Physical, Mechanical and Perforation Resistance of Natural-Synthetic Fiber Interply Laminate Hybrid Composites

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