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Manisha Tanwer,
Tanya Singh,
Amar Mohite,
Sridevi Sangeetha K S,
Mrunmai Mandar Ranade,
Abhishek Dixit,
- Associate Professor, Department of Basic Science and Humanities, D Y Patil College of Engineering Akurdi, Pune, Maharashtra, India
- Associate Professor, Department of Engineering, School of Engineering & Technology, Noida international University, Greater Noida, Uttar Pradesh, India
- 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 Allied Helath, Meenakshi College of Allied Health Sciences, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research, Kanchipuram, Tamil Nadu, India
- Associate Professor, Department of Engineering Sciences, Vishwakarma University, Pune, Maharashtra, India
- Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
Abstract
The development of sustainable biomedical polymer composites has become one solution that can be used to combat increasing environmental issues that have been presented by traditional medical materials without compromising functional performance. Implementation of the artificial intelligence (AI) in material design presents a paradigm shift of data-driven development, which improves the efficiency, accuracy, and scalability of composite development. The given work can serve as a universal guideline in developing biodegradable polymer composites based on machine learning and optimization algorithms. In the proposed method, the choice of the material, features engineering, predictive modeling, and validation will be involved in order to attain the optimum mechanical, biological, and environmental characteristics. Lifecycle assessment (LCA) is incorporated to assess the environmental impact at each stage in order to be sustainable at every stage of production up to disposal. The methods of green manufacturing are also investigated to reduce the use of energy and minimize the use of waste chemicals. According to case studies, tensile strength, biocompatibility, and degradation can be dramatically enhanced using AI-driven optimization over traditional ones. Also, the framework aids in the determination of the most effective processing parameters, which increases the performance and resource efficiency. The results point to the opportunities of the integration of AI and sustainable material design in order to speed up the innovation in biomedical engineering. The paper can lead to the creation of biomaterials with high performance and environmentally friendliness and also offers a base on future developments of intelligent and sustainable technologies in healthcare.
Keywords: Artificial Intelligence, Biomedical Polymer Composites, Sustainable Materials, Machine Learning, Biodegradable Polymers, Lifecycle Assessment, Green Manufacturing, Predictive Modeling.
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Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 01/05/2026 | |
| Accepted | 05/06/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 116 Days |