ItemArticle

Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN

Title
Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN
Creator
Singhal, Abhinav; Saeed, Abdulkafi Mohammed; Seema; Ahmad, Naved; Sharma, Shweta; Chaudhary, Anjali
Description
This study develops a semi-analytical framework to investigate the dynamic response of semi-elliptical notches in piezoelectric half-spaces subjected to shear-horizontal (SH) wave excitation. By employing wave function expansions in elliptical coordinates and Mathieu functions, the model efficiently solves boundary value problems in electromechanically coupled media and demonstrates greater versatility compared to conventional techniques. The analysis highlights how notch depth, wave incidence angle, and excitation frequency govern surface displacement and stress amplification. In particular, deeper notches under high-frequency excitation yield pronounced dynamic stress concentration, which raises concerns regarding the structural integrity of piezoelectric devices. Comparative results further reveal that materials with stronger piezoelectric coupling, such as PZT-5H, exhibit more severe stress localization than PZT-6B or BaTiO?. The study also examines the role of weak interfaces and nanoscale surface effects. Weak interfaces are shown to reduce stiffness in phonon and phason fields while increasing stiffness in the electric field for Rayleigh waves, with such effects becoming most prominent under strongly dispersive conditions. At the nanoscale, surface and interface influences effectively mitigate dynamic stress concentration, with diffraction stress concentration factor (DSCF) decreasing monotonically as the nano-influence factor increases, eventually tending to vanish in the limit of diminishing defect size. To complement the analytical formulation, a K-Nearest Neighbors (KNN) machine learning (ML) model was implemented using the analytical DSCF dataset. The classifier achieved nearly 90% accuracy in distinguishing between low and high stress concentration regimes. Decision maps highlighted frequencygeometry combinations most prone to defect amplification, while the confusion matrix confirmed reliable detection of critical hot-spots. This integration of ML provides a rapid surrogate framework that complements the semi-analytical method, enabling efficient prediction, defect screening, and design optimization in advanced piezoelectric systems. The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Source
Journal of Engineering Mathematics;Volume;154;Issue;1;Article No.;9;
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Dynamic stress concentration; K-nearest neighbors (KNN); Machine learning surrogate; Mathieu function; Nanoscale surface effects; Piezoelectric material; Semi-elliptical notches; SH Waves; Thin-walled structure; Weak interface effects
Coverage
Singhal A., Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Seema, Christ University, Bengaluru, 560029, India; Ahmad N., School of Computer Science and Engineering, IILM University Gurugram, Gurugram, India; Sharma S., Department of Applied Sciences and Humanities, ABES Engineering College, Ghaziabad, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 220833;
Format
online
Language
English
Type
Article

Citation

Singhal, Abhinav; Saeed, Abdulkafi Mohammed; Seema; Ahmad, Naved; Sharma, Shweta; Chaudhary, Anjali, “Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/21880.

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