Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision

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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.

Year : 2026 | Volume : 14 | 04 | Page :
By

Priyadarshani A. Patil,

Nikhil Mohan Shinde,

Swetha,

M Sudhakar,

Manjula Devarakonda Venkata,

Neha Rana,

  1. Associate Professor, Department of Science, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara, Maharashtra, India
  2. Associate Professor, Department of Mechanical Engineering, Vishwakarma University, Pune, Maharashtra, India
  3. Associate Professor, Department of Science, College of Allied Health Sciences, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research, Kanchipuram, Tamil Nadu, India
  4. Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India
  5. Associate Professor, Department of Computer Science and Engineering, Pragati Engineering College, Kakinada District, Andhra Pradesh, India
  6. Associate Professor, Department of Pharmacy, School of Pharmacy, Noida international University, Greater Noida, Uttar Pradesh, India

Abstract

The biomedical composite materials are intelligent failure-detecting, which is necessary to ensure the reliability, safety, and durability of the current healthcare equipment. This paper describes a machine vision design, which incorporates convolutional neural networks, transformer models, and ensemble learning to correctly detect and localize material defects. The proposed system takes advantage of the capabilities of high-resolution imaging, advanced preprocessing software, and deep feature learning in the identification of the intricate shape of failures such as micro-cracks, delamination, and porosity. An explainable artificial intelligence component is also used to enhance the degree of interpretability to offer a visualization tool and make decisions in a transparent and reliable way. An edge based cloud collaborative architecture is used to deploy the framework in order to provide real time performance and scalability. The experimental study indicates that the hybrid model has been demonstrated to be more efficient when compared to traditional approaches, since it has better accuracy, increased sensitivity and good generalization in various defect scenarios. The fact that performance improvement is crucial is statistically confirmed and the analysis of errors would contribute to the realization of the limitations of the models and their optimalization. The combination of smart machine vision and biomedical composite monitoring has a way to go to offer predictive maintenance and smart healthcare systems to develop the opportunity to identify the failures early enough and improve clinical outcomes.

Keywords: Biomedical Composite Materials, Machine Vision, Failure Detection, Deep Learning, Convolutional Neural Networks, Transformer Models, Ensemble Learning, Explainable AI, Edge–Cloud Computing, Predictive Maintenance.

How to cite this article: Priyadarshani A. Patil, Nikhil Mohan Shinde, Swetha, M Sudhakar, Manjula Devarakonda Venkata, Neha Rana. Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: Priyadarshani A. Patil, Nikhil Mohan Shinde, Swetha, M Sudhakar, Manjula Devarakonda Venkata, Neha Rana. Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=253309

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Ahead of Print Subscription Original Research
Volume 14
04
Received 01/05/2026
Accepted 22/06/2026
Published 25/08/2026
Publication Time 116 Days

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