@Article{cmes.2026.086198,
AUTHOR = {Rahul Kumar, Faladrum Sharma, Pradeep Kumar Karsh},
TITLE = {A Review on Machine Learning and Digital Twin Enabled Advancements in Biofiller/Fibre-Based Polymer Composites for Sustainable Engineering Applications},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {2},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v148n2/68590},
ISSN = {1526-1506},
ABSTRACT = {The increasing environmental concerns after the United Nations’ push towards sustainable development goals (SDGs) and depletion of non-renewable resources have accelerated the global pursuit of sustainable materials. Within this framework, bio-based polymer composites have gained considerable attention for their ability to balance mechanical performance, cost-effectiveness, and environmental responsibility. Biofibres/fillers-based polymer composites reinforced with natural fibres like jute, bamboo, coconut coir, pineapple leaf fibre (PALF), and flax offer an attractive combination of mechanical performance, cost-effectiveness, and environmental sustainability. Moreover, additive manufacturing (3D printing), coupled with machine learning, digital twins, and data-driven material design, is transforming the development and optimization of natural fibre-reinforced polymer composites for advanced, sustainable engineering applications and enabled the realization of the material, structure and function paradigm for composite design. This review highlights major developments in bio-based composites through the perspective of integrated modelling strategy and material-design-function philosophy. It also outlines how machine learning and digital twin-based models accelerated the material discovery, prediction, optimization, and sustainability assessment. Using machine learning techniques, prediction of properties, optimization of processes, and detection of defects can be performed in a much faster way. Digital twins provide a virtual representation of components, enabling real-time monitoring, predictive maintenance, virtual testing, and full lifecycle analysis. Moreover, the purpose of this review is to organize the latest progress, highlight the main research gaps, and propose the next steps towards smart, eco-friendly, and Industry 4.0/5.0, compliant composite materials. Also, the integration of life cycle assessment data into digital twin platforms could enable continuous environmental performance assessment throughout the product lifecycle.},
DOI = {10.32604/cmes.2026.086198}
}