TY - JOUR
T1 - Sustainable Medical Materials
T2 - AI-Driven Assessment for Mechanical Performance of UVC-Treated Date Palm Epoxy Composites
AU - Aboamer, Mohamed A.
AU - Hakami, Abdulrahman
AU - Algethami, Meshari
AU - Alarifi, Ibrahim M.
AU - El-Bagory, Tarek M.A.A.
AU - Alassaf, Ahmad
AU - Alresheedi, Bakheet A.
AU - AlOmari, Ahmad K.
AU - Almazrua, Abdulaziz Abdullah
AU - ABDELRAHMAN SHAABAN, NADER
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/4
Y1 - 2025/4
N2 - This study investigates the AI-assisted analyses of radiation disinfection effects on the mechanical properties of recycled date kernel powder–epoxy composites for medical applications, utilizing Euclidean distances and the k-nearest neighbor (KNN) algorithm. Tensile and compression tests were conducted on twenty specimens following ASTM standards, with the data analyzed using a t-test to evaluate the impact of the UVC disinfection process on the material’s mechanical properties. The application of AI through the KNN algorithm successfully identified the three most representative curves out of five for both tensile and compression tests. This targeted curve selection minimized variability and focused on the most relevant data, enhancing the reliability of the analysis. Following the application of UVC and AI, tensile tests showed a 20–30% increase in ultimate stress. Similarly, compression tests revealed a 25% increase in transition stress, an 18–22% improvement in ultimate stress, and approximately a 12% rise in fracture stress. This research underscores the potential of combining AI, sustainable materials, and UVC technology to develop advanced composites for medical applications. The proposed methodology offers a robust framework for evaluating material performance while promoting the creation of eco-friendly, high-performance materials that meet the stringent standards of medical use.
AB - This study investigates the AI-assisted analyses of radiation disinfection effects on the mechanical properties of recycled date kernel powder–epoxy composites for medical applications, utilizing Euclidean distances and the k-nearest neighbor (KNN) algorithm. Tensile and compression tests were conducted on twenty specimens following ASTM standards, with the data analyzed using a t-test to evaluate the impact of the UVC disinfection process on the material’s mechanical properties. The application of AI through the KNN algorithm successfully identified the three most representative curves out of five for both tensile and compression tests. This targeted curve selection minimized variability and focused on the most relevant data, enhancing the reliability of the analysis. Following the application of UVC and AI, tensile tests showed a 20–30% increase in ultimate stress. Similarly, compression tests revealed a 25% increase in transition stress, an 18–22% improvement in ultimate stress, and approximately a 12% rise in fracture stress. This research underscores the potential of combining AI, sustainable materials, and UVC technology to develop advanced composites for medical applications. The proposed methodology offers a robust framework for evaluating material performance while promoting the creation of eco-friendly, high-performance materials that meet the stringent standards of medical use.
KW - AI-assisted analysis
KW - mechanical properties
KW - medical applications
KW - recycled composites
KW - UVC disinfection
UR - http://www.scopus.com/inward/record.url?scp=105003652060&partnerID=8YFLogxK
U2 - 10.3390/polym17081125
DO - 10.3390/polym17081125
M3 - Article
AN - SCOPUS:105003652060
SN - 2073-4360
VL - 17
JO - Polymers
JF - Polymers
IS - 8
M1 - 1125
ER -