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Dipali S. Mankar,
Sanjesh Sadanand Pawale,
Pugazhendhi S,
Naveen Jain,
ANUDEEP MEDA,
M Sunil Raj,
- Associate Professor, Department of Engineering, PCETs, Pimpri Chinchwad University, Pune, Maharashtra, India
- Associate Professor, Department of Computer Engineering, Vishwakarma University, Pune, Maharashtra, India
- Associate Professor, Department of Pharmacology, Meenakshi College of Pharmacy, Meenakshi Academy of Higher Education and Research, Mevalurkuppam, Tamil Nadu, India
- Associate Professor, Department of Mechanical Engineering, Shri Shankaracharya Institute of Professional Management & Technology, Raipur, Chhattisgarh, India
- Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
- Associate Professor, Department of Mechanical Engineering, Pragati Engineering College, Kakinada District, Andhra Pradesh, India
Abstract
Artificial Intelligence (AI) has already become a ground-breaking tool of streamlining polymer composite implants to enhance both mechanical strength and biocompatibility simultaneously. This paper recommend an AI-based multi-objective optimization model, which integrates the selection of materials, structural modelling, and biological evaluation. The in vitro biocompatibility indicators, including cytotoxicity and cell adhesion, can be used to model mechanical behavior, e.g. stress-strain behavior and fatigue behavior. To arrive at an optimal material compositions and processing parameters, the use of advanced algorithms which includes genetic algorithms, neural networks etc are used. Experimental validation shows an improved performance trade-off and therefore has been more stable and biological compatible hence contributing to the creation of next-generation implantable biomedical devices with a dependable performance. The proposed framework incorporates the material selection, predictive modelling and multi-objective optimization to arrive at the optimal design configurations. The methodology is supported by the findings that demonstrated significant advances of strength, fatigue resistance and bio-compatibility. The fact that it can be used as the solution to conflicting requirements at the same time makes the implants more reliable and medically applicable. Furthermore, AI-related methods guarantee that time and money wasted in experiments are reduced, as well as increase accuracy. The future of work can be addressed in terms of the adaptive optimization and integration of the advanced manufacturing methodology that can be further added to the personalized design of implants and biomedical results in real-time.
Keywords: Artificial Intelligence, Polymer Composite Implants, Mechanical Properties, Biocompatibility, Multi-objective Optimization, Genetic Algorithms.
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Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 01/05/2026 | |
| Accepted | 13/07/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 116 Days |