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, 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 behavior. 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. 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 potential 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.
[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]
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Journal of Polymer & Composites
| Volume | 14 | |
| Special Issue | 04 | |
| Received | 01/05/2026 | |
| Accepted | 03/07/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 116 Days |