ArticlePolymers2024
Development of an Analytical Model for Predicting the Tensile Modulus of Complex Polypropylene Compounds.
Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Use of Artificial Neural Networks for Recycled Pellets Identification: Polypropylene-Based Composites.Polymers · 2025Article
- Article
- Model-Based Prediction of the Tensile Properties of Polypropylene Films Made from Recycled Materials.Polymers · 2025Article
- Development of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds.Polymers · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The extensive use of polypropylene (PP) in various industries necessitates the development of efficient and reliable methods for predicting the mechanical properties of PP compounds. This study presents the development of an analytical model (AM) designed to predict the tensile modulus for a dataset of 64 PP compounds with various fillers and additives, including chalk, impact strength modifiers, and peroxide additives. The AM, incorporating both logarithmic and linear components, was benchmarked against an artificial neural network (ANN) to evaluate its performance. The results demonstrate that the AM consistently outperforms the ANN, achieving lower mean absolute error (MAE) and higher coefficient of determination (R
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Registered trials
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