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.
Santosh Laxman Pachpute,
V.M. Thorat,
Amruta Tejaskumar Mokashi,
Sagayaraj P,
M Rama Chandra Rao,
A. Phani Bhaskar,
- Assistant Professor, Department of Mechanical Engineering, D. Y. Patil College of Engineering, Akurdi. Akurdi, Pune, Maharashtra, 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 Chemical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
- Associate Professor, Department of Science, 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
- Associate Professor, Department of Mechanical Engineering, Pragati Engineering College, Kakinada District, Andhra Pradesh, India
Abstract
Another potential solution to improving the results of tissue engineering is smart polymer composite scaffolds, which are capable of dynamic adaptation to changing biological factors, but typical scaffolds cannot change dynamically. This paper suggests a comprehensive system to integrate biodegradable polymer composite scaffolds with sensing and machine learning-based feedback to allow the real-time monitoring and active regulation of tissue regeneration events. The system uses biocompatible materials of PLA/PCL composite of bioactive filler, as well as, embedded sensors to measure physiological parameters like pH, temperature, and mechanical strain in continuous mode. A machine learning model is created to forecast the behavior of tissue growth and provide feedback on scaffold behavior optimization by using a closed-loop control approach. Experimental analysis indicates that there are considerable increases in predictive performance and biological performance. The proposed system has a reduction in the prediction error where RMSE = 0.109 and (R 2 ) = 0.94 which is better than baseline scaffold systems by a significant margin. The cell proliferation index rises by 0.72 to 0.88 which shows the increased tissue growth consistency. The real-time feedback allows the swift response to the system in less than 2.5 minutes, thus, being able to adapt to the environmental changes. The findings confirm that incorporating machine learning and smart biomaterials into the precision tissue engineering process is effective. The system offers a personalized medicine and regenerative therapies scaling and modular solution. The additions to be made in the future are deep learning, IoT-based monitoring, and clinical validation to use the applications in a real-life setting.
Keywords: Smart Polymer Composite Scaffolds, Tissue Engineering, Machine Learning Feedback, Biodegradable Materials, Embedded Sensors, Real-Time Monitoring, Adaptive Control Systems, Predictive Modeling, Regenerative Medicine, Intelligent Biomaterials
References
- Filippi M, Born G, Chaaban M, Scherberich A. Natural polymeric scaffolds in bone regeneration. Front Bioeng Biotechnol. 2020;8:474.
- Nikolova MP, Chavali MS. Recent advances in biomaterials for 3D scaffolds: a review. Bioact Mater. 2019;4:271-292.
- Szymczyk-Ziółkowska P, et al. A review of fabrication polymer scaffolds for biomedical applications using additive manufacturing techniques. Biocybern Biomed Eng. 2020;40:624-638.
- Jain R, Shetty S, Yadav KS. Unfolding the electrospinning potential of biopolymers for preparation of nanofibers. J Drug Deliv Sci Technol. 2020;57:101604.
- Mattioli-Belmonte M, et al. Tailoring biomaterial compatibility: in vivo tissue response versus in vitro cell behavior. Int J Artif Organs. 2003;26:1077-1085.
- Gholap AD, et al. Chitosan scaffolds: expanding horizons in biomedical applications. Carbohydr Polym. 2024;323:121394.
- Abdullah MF, et al. Core-shell fibers: design, roles, and controllable release strategies in tissue engineering and drug delivery. Polymers (Basel). 2019;11:2008.
- Echave MC, et al. Enzymatic crosslinked gelatin 3D scaffolds for bone tissue engineering. Int J Pharm. 2019;562:151-161.
- Ghorbani F, Li D, Ni S, Zhou Y, Yu B. 3D printing of acellular scaffolds for bone defect regeneration: a review. Mater Today Commun. 2020;22.
- Haleem A, Javaid M, Khan RH, Suman R. 3D printing applications in bone tissue engineering. J Clin Orthop Trauma. 2020;11:S118-S124.
- Bankar A, Sonekar AM, Kashyap AR, Pathan AA, Ansari RS. Effect of print speed, infill pattern and infill density on tensile strength of part produced by ABS filament using FDM 3D printing technology. IJTARME. 2025;14(1):41-47.
- Aldana A, Abraham GA. Current advances in electrospun gelatin-based scaffolds for tissue engineering applications. Int J Pharm. 2017;523:441-453.
- Moehring HC, et al. The additive-subtractive process chain—a review. J Mach Eng. 2023;23:5-35.
- Sathish K, et al. A comparative study on subtractive manufacturing and additive manufacturing. Adv Mater Sci Eng. 2022;2022:6892641.
- Krishani M, Shin WY, Suhaimi H, Sambudi NS. Development of scaffolds from bio-based natural materials for tissue regeneration applications: a review. Gels. 2023;9:100.
Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 01/05/2026 | |
| Accepted | 30/06/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 116 Days |