Prediction of Strength Properties of Elastic Polymeric Composite Materials Based on Polypropylene Woven Tapes

Prokhorova, I. A.; Bkkar, Muhammad Ahmad

doi:10.1007/s10692-020-10175-w

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…The problem of predicting the properties of PCMs can be solved both by developing new and effective mathematical and numerical research methods [ 54 ] and with the level of understanding of the physicochemical processes that determine the mechanical properties of these complex materials [ 55 ]. Methods for calculating composite materials are based on solving the equations of solid medium mechanics.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions

et al. 2022

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The technical and economic efficiency of new PCMs depends on the ability to predict their performance. The problem of predicting the properties of PCMs can be solved by computer simulation by the finite element method. In this work, an experimental determination of the physical and mechanical properties of PTFE PCMs depending on the concentration of fibrous and dispersed filler was carried out. A finite element model in ANSYS APDL was built to simulate the strength and load-bearing capacity of the material with the analysis of damage accumulation. Verification of the developed computer model to predict the mechanical properties of composite materials was performed by comparing the results obtained during field and model experiments. It was found that the finite element model predicts the strength of chaotically reinforced spherical inclusions of composite materials. This is due to the smoothness of the filler surfaces and the lack of filler dissection in the model. Instead, the prediction of the strength of a finite element model of chaotically reinforced cylindrical inclusions of composite materials requires additional analysis. The matrix and the fibrous filler obviously have stress concentrators and are both subject to the difficulties of creating a reliable structural model.

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Section: Introductionmentioning

confidence: 99%

Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions

et al. 2022

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“…Among the composite resin, polypropylene composites (PPCs) are widely used in various areas and in numerous applications, such as electrical equipment, laboratory equipment, textiles, reusable containers, aerospace structures, infrastructure, pipes, and tanks. [1][2][3][4][5][6][7] PPCs have many merits with flowability, mechanical characteristics, weatherability, and chemical resistance, and are economical considering cost aspects. 8 Because of the versatility of PPCs, the required physical properties are countless and vary depending on the purpose of end products.…”

Section: Introductionmentioning

confidence: 99%

“…One of the representative composite materials is the composite resin. Among the composite resin, polypropylene composites (PPCs) are widely used in various areas and in numerous applications, such as electrical equipment, laboratory equipment, textiles, reusable containers, aerospace structures, infrastructure, pipes, and tanks 1–7 . PPCs have many merits with flowability, mechanical characteristics, weatherability, and chemical resistance, and are economical considering cost aspects 8 …”

Section: Introductionmentioning

confidence: 99%

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Joo

Park

Lim

et al. 2021

Int J of Intelligent Sys

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The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision‐making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.

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Prediction Model of Mechanical Properties of Elastic Composites Based on Machine Learning Algorithm

Hu,

Zhang,

et al. 2024

Learning and Analytics in Intelligent Systems

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Prediction of Strength Properties of Elastic Polymeric Composite Materials Based on Polypropylene Woven Tapes

Cited by 3 publications

References 1 publication

Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions

Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Prediction Model of Mechanical Properties of Elastic Composites Based on Machine Learning Algorithm

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