Sustainable Polymer Composites: Bio-Additive Reinforced Glass Fiber Materials for Household Utensils Optimization Using Machine Learning Approach
Küçük Resim Yok
Tarih
2026
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The escalating global consumption of plastics necessitates a paradigm shift toward sustainable materials that do not compromise on performance. This research pioneers the development of bio-additive polymer composites for kitchenware, employing a data-driven methodology to optimize their formulation and properties. Four distinct composites were engineered using polypropylene (PP) and acrylonitrile butadiene styrene (ABS) matrices, bio-enhanced with coffee dust or tea fiber (2%), and reinforced with glass fiber (12%). These formulations PPC, PPCGF, ABSTF, and ABSTFG were fabricated via injection molding and rigorously characterized through tensile, flexural, and impact testing. A cornerstone of this study was the implementation of a Python-based analytical framework for the critical evaluation of the sample data. Custom algorithms were developed to process and correlate the multi-faceted experimental results, mechanical properties, melt flow, and 3D optical dimensions. This computational approach enabled the application of machine learning models to identify optimal process parameters and predict mechanical performance. Furthermore, correlation analysis and principal component analysis (PCA) quantitatively deconstructed the complex interplay of material constituents, validating the dominant role of glass fiber in enhancing composite stiffness and strength. The findings not only demonstrate a viable pathway for high-performance, eco-compatible composites but also establish a robust digital blueprint for accelerated material innovation.
Açıklama
Anahtar Kelimeler
[Keyword Not Available]
Kaynak
Journal of Applied Polymer Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
143
Sayı
26
