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

Sinha, Agnivesh Kumar; Narang, Harendra Kumar; Bhattacharya, Sandip

doi:10.1007/s40430-020-02377-4

Cited by 17 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step II: Defining the membership functions of each variable Membership functions play a vital role in mapping the non-fuzzy input values to fuzzy linguistic terms during fuzzification and defuzzification respectively. A linguistic term was quantified by a membership function [11]. Triangular membership function having gradual increase and decrease characteristics has been used in this study.…”

Section: Fuzzy Logic Approachmentioning

confidence: 99%

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Jagadeesh¹,

Kumar²,

Aneesh³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

The application spectrum of natural fiber reinforced polymer composites has been increasing tremendously because of enhanced tribologicalbehavior. The present study investigates the wear and coefficient of friction of castor oil fiber reinforced composites for developing as new tribo-material. Composites with 40 vol.% with short fiber length of 5mm were fabricated by hand lay-up method. The tribology tests were performed with pin on disc tribometer at normal loads (15N, 30N & 45N) and sliding distances (1000m, 2000m & 3000m). A fuzzy logic model has been developed to predict and analyze the wear and friction characteristics at unknown test cases. Each input variable was divided into three linguistic variables and each output variable was divided into five linguistic variables. A triangular membership function was used for defining all the variables. The capabilities of fuzzy logic model were tested by confirmatory experiments. The model predicted the wear results with an error of 3.12% and coefficient of friction by 2.19%.

show abstract

Section: Fuzzy Logic Approachmentioning

confidence: 99%

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Jagadeesh¹,

Kumar²,

Aneesh³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…25 A fuzzy approach was used to determine the modeling of mechanical behaviors of composites. 26 The aforementioned authors have prepared natural fiber-based composites with synthetic fiber. Various properties of the composites were ameliorated with the help of various techniques such as fiber treatment and fiber hybridization.…”

Section: Introductionmentioning

confidence: 99%

Effect of fiber hybridization on mechanical, thermal, and water absorption behavior of HF/CF/HDPE composites

Srivastava

Sinha

2021

Polymers and Polymer Composites

View full text Add to dashboard Cite

The composite materials are present in nature since the prehistoric era. Applications of fiber-based composite materials are increasing day by day in our society to overcome the raised environmental and economic concerns. Hence, waste fiber can be utilized as the best resource to develop composites. The present study deals with the impact of hybridization on the mechanical, thermal, and water absorption behavior of hair and coir fiber-based hybrid composites. The compression molding technique was used to develop the hybrid composites with fixed fiber content (15 wt.%) and was also varied the relative weight percentage of hair and coir fibers [(100% HF), (75% HF/25% CF), (50% HF/50% CF), (25% HF/75% CF) and (100% CF)] in reinforcing phase with HDPE composites S1, S2, S3, S4, and S5, respectively. The composite S2 was achieved superior mechanical attributes as compared to other hybrid/non-hybrid composites. The composite S2 was improved the tensile strength 5% and 35.2% more in comparison to composites S1 and S5, respectively. The thermal behavior (TGA, DTG, and DTA) was also influenced by the blending ratio of fibers of composites. The 5% and 50% weight losses of composite S2 were observed at higher temperature 343.8°C and 465.8°C as compare to other composites, which showed the thermal stability of composites S2. SEM analysis was used to investigate the strength of the fiber-matrix interface, which was shown a significant connection between mechanical and thermal behaviors. The crystallinity of hybrid and non-hybrid composites was examined by using the X-ray diffraction (XRD) analyzer and composite S2 was achieved 326 × 10−9 m crystal size at 21.053° peak position with wavelength 1.5406 × 10−10 m for Cu. The water absorption test was used to examine the moisture resistivity of composite materials, which was helpful to increase the applications of materials in humid areas.

show abstract

“…Prediction applications range from materials properties prediction, prediction of chemical process outputs, prediction of industrial processes outputs etc. Often prediction models whether mathematical or computational are sought because experiments are expensive to conduct [1,2,3]. Development of excellent prediction and optimization models for materials science and engineering applications is critical for advances in modern engineering.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the fuzzy logic model predicted the experimental results with an accuracy of 94.45%. Sinha et al [1] used fuzzy logic for the prediction of the mechanical properties of hybrid abaca-reinforced polymer composite. The input variables for the prediction model were weight per cent of abaca fibre, particle size of red mud and weight per cent of red mud while the output parameters were tensile strength and flexural strength.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Properties of Sponge Gourd-Bagasse Hybrid Reinforced Polymer Composite Using Fuzzy Logic, PSO, Modified Desirability Function and NSGA II

Chibueze

Obele

Nwobi-Okoye

et al. 2021

Preprint

View full text Add to dashboard Cite

Development of mathematical models for prediction of properties of materials is often complex and cumbersome. This led to the advent of simpler, and often more accurate, computational models based on artificial intelligence for predicting materials properties. The aim of this study is to predict the mechanical properties of a newly developed hybrid composite material made with sponge gourd, baggase and epoxy resin for golf club application using fuzzy logic (FL) and carry out a multi-objective optimization of the properties with modified desirability function (DF) and NSGA II algorithm. The inputs were %Wt of baggase, %Wt of Sponge gourd and Fiber size (µm) while the response variables were tensile strength, hardness, flexural strength, modulus, elongation and impact strength. The FL model was separately coupled, as fitness function, with the modified DF algorithm and the NSGA II algorithm respectively. The DF was optimized with particle swarm optimization (PSO) algorithm. The results showed that the FL model predicted the mechanical properties accurately and the minimum correlation coefficient (R) between the experimental responses and FL predictions was 0.9529. The modified algorithms took care of certain peculiarities in the desirability properties such as elongation whose desirability is constant over a range. The optimized properties were found to be worse if the optimization algorithms were not modified.

show abstract

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

Cited by 17 publications

References 25 publications

A fuzzy logic approach for investigating the tribological behavior of polymer composite

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Effect of fiber hybridization on mechanical, thermal, and water absorption behavior of HF/CF/HDPE composites

Multi-Objective Optimization of Properties of Sponge Gourd-Bagasse Hybrid Reinforced Polymer Composite Using Fuzzy Logic, PSO, Modified Desirability Function and NSGA II

Contact Info

Product

Resources

About