This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Manisha Tanwer,
Narendrakumar J. Suryavanshi,
Debanjana Prasad,
Prabhavathy Devi N,
M Rama Chandra Rao,
M. Radhika Mani,
- Associate Professor, Department of Basic Science and Humanities, D Y Patil College of Engineering Akurdi, Pune, Maharashtra,
- Associate Professor, Department of Science, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara, Maharashtra, India
- Associate Professor, Department of Biotechnology, Noida international University, Greater Noida, Uttar Pradesh, India
- Associate Professor, Department of Nutrition and Dietetics, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
- Professor, Department of Computer Science and Engineering, Pragati Engineering College, Kakinada District, Andhra Pradesh, India
Abstract
The value of biodegradable polymer composites in the medical practice has been massive as the composites may be deployed to provide temporary structural support, and they are also safe to degrade within the human body. However, the conventional material design process is trial and error, which is ineffective and inefficient. The article proposes a hybrid model involving experimental characterization, as well as an artificial intelligence (AI)-based model, to optimize biodegradable polymer composites. Polylactic acid (PLA) and polycaprolactone (PCL) are some of the bioactive filled polymers that are being investigated to attain a more appropriate mechanical and degradation behaviour. Machine learning models are random Forest and Artificial Neural Networks, which are used to predict the properties of the materials based on the factors of composition and processing. The results are very precise in prediction and correlation with the experimental findings. The Multi objective optimization enables identification of the best composite structures that are improved in tensile strength and controlled degradation. The tests of comparative analysis can confirm the hypothesis that the AI-based method can significantly reduce time and experiment work on the development process and enhance the performance of materials. The given framework discusses the potentials of AI-inspired material design as the means of creating biodegradable biomaterials in tissue engineering, drug delivery, and implantable medical devices.
Keywords: Biodegradable Polymer Composites, Artificial Intelligence, Machine Learning, Biomaterials, Tissue Engineering, Drug Delivery, Material Optimization
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Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 01/05/2026 | |
| Accepted | 03/07/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 116 Days |