Influence of UV irradiation on mechanical properties and drop-weight impact performance of polypropylene biocomposites reinforced with short flax and pine fibers

Nasri, Khaled; Toubal, Lotfi; Loranger, Éric; Koffi, Demagna

doi:10.1016/j.jcomc.2022.100296

Cited by 8 publications

(17 citation statements)

References 41 publications

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“…Both biocomposites exhibited changes in their physical and mechanical properties over time. These changes were more significant under the second treatment (UV irradiation with moisture), possibly because photo-oxidation reactions are the main contributors to SNFT property degradation [ 10 ], and moisture accelerates these reactions [ 23 ]. Consequently, a higher degree of degradation was observed under the second condition (UV irradiation with moisture) for both materials.…”

Section: Resultsmentioning

confidence: 99%

“…Flax fibers differ from pine fibers with regard to their chemical and geometric compositions. Flax fibers are rich in cellulose and have a higher length-to-diameter (L/d) ratio than pine fibers [ 10 ], whereas pine fibers are richer in lignin than flax fibers. This difference in fiber composition may influence the mechanical and aging properties of the studied short-fiber composites.…”

Section: Experimentationmentioning

confidence: 99%

“…SNFT biocomposites offer high-specific mechanical properties and are eco-friendly [ 5 , 6 , 7 , 8 ]. Nevertheless, one major challenge in developing natural-fiber composites is their response to environmental factors, such as moisture, ultraviolet (UV) radiation, and heat, which can degrade their mechanical performance and limit their applicability, especially for outdoor applications [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have extensively investigated the aging of SNFTs via exposure to real climates (natural aging) [ 16 , 17 , 18 , 19 ] or simulated conditions in laboratory chambers (artificial aging) [ 20 , 21 , 22 ]. When SNFTs are exposed to UV rays and/or high temperatures, photo-oxidation reactions occur in the lignin of natural fibers, and moisture accelerates these reactions [ 10 , 23 ]. The velocity of photo-oxidation reactions in biocomposites depends on the chemical composition of the natural fibers.…”

Section: Introductionmentioning

confidence: 99%

“…They revealed that the lignin in natural fibers can act as a UV absorber, mitigating the degradation of the mechanical properties. Similarly, Nasri et al [ 10 ] reported that biocomposites reinforced with wood pine fibers, which have high lignin content, exhibited less significant degradation in mechanical properties than flax fibers. For fibers, UV exposure initiates aging by causing absorption in the lignin structure, forming chromophore groups, like carboxylic acids, quinones, and hydroperoxy radicals, resulting in discoloration [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

Nasri,

Toubal

2024

Polymers

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Amidst escalating environmental concerns, short natural-fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load, and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% with regard to the impact load, absorbed energy, and tensile strength, respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

Nasri,

Toubal

2024

Polymers

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Assessing the impact of accelerated weathering on the mechanical properties of a flax/epoxy composite: A study on tensile performance and drop-weight impact

Hadj-Djilani,

Bougherara,

Zitoune

et al. 2024

Composites Part A: Applied Science and Manufacturing

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Artificial Neural Network Approach for Assessing mechanical properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

Nasri,

Toubal

2024

Preprint

View full text Add to dashboard Cite

Amidst escalating environmental concerns, short natural fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% about the impact load, absorbed energy, and tensile strength, respectively.

show abstract

Influence of UV irradiation on mechanical properties and drop-weight impact performance of polypropylene biocomposites reinforced with short flax and pine fibers

Cited by 8 publications

References 41 publications

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

Assessing the impact of accelerated weathering on the mechanical properties of a flax/epoxy composite: A study on tensile performance and drop-weight impact

Artificial Neural Network Approach for Assessing mechanical properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation

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