AI-Driven Machine Learning Model for Stress Classification in Thin Film Materials
| Authors | Laxmikant S. Dhamande1, Shashikant Raghunathrao Deshmukh2, Tarun Dhar Diwan3 , Yogeesh N4, P. William5 , Sandip R. Thorat1, Abhishek Badholia6 |
| Affiliations |
1Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 2Department of Computer Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 3Controller of Examination (COE), Atal Bihari Vajpayee University, Bilaspur, India 4Department of Mathematics, Government First Grade College, Tumkur, Karnataka, India 5Department of Information Technology, Sanjivani College of Engineering, Kopargaon, MH, India 6Department of Data Science, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, India |
| Е-mail | tarunctech@gmail.com |
| Issue | Volume 17, Year 2025, Number 5 |
| Dates | Received 10 August 2025; revised manuscript received 22 October 2025; published online 30 October 2025 |
| Citation | Laxmikant S. Dhamande, Shashikant Raghunathrao Deshmukh, Tarun Dhar Diwan, et al., J. Nano- Electron. Phys. 17 No 5, 05036 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(5).05036 |
| PACS Number(s) | 07.05.Mh, 68.55. – a, 68.55.J – |
| Keywords | Stress classification, Thin film materials, Refined Crayfish Optimised K-Nearest Neighbor (RCO-KNN), Z-Score normalization. |
| Annotation |
In a range of practical applications, which contain microelectronics then aerospace engineering, the sorting of stress in thin film material is dynamic for maximizing their presentation and dependability. By integrating revolutionary data training methods with a unique classification, this study suggests an AI-driven Machine Learning (ML) method for stress classification in thin film material. To promise high-quality input for the knowledge of manner, the technique uses data cleaning strategies to cast off noise and outliers. Z-score normalization is used to standardize the data, expanding the technique's applicability to a wider variety of information. This study presents the Refined Crayfish Optimised K-Nearest Neighbor (RCO-KNN) algorithm for the enterprise job that is scheduled to raise the precision and resilience of strain detection in skinny movie materials. The RCO-KNN approach advances at the KNN by making use of Crayfish Optimization, which maximizes the selection of neighbors and distance metrics to guarantee accurate classification. This ROC-KNN achieved high accuracy (98.5 %), precision (96.2 %), recall (95.6 %), and F1-score (97.8 %). According to experimental results, this information gives a tremendously dependable tool for material science programs in guessing out the stress levels in thin film materials. |
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List of References |
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