[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125692-en":3,"doc-seo-125692-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125692,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach","Piezoelectric actuators are applied as an effective route for repairing cracks in thin-walled structures, including plates bonded with piezoelectric patches through electromechanical action. A finite element study with the ANSYS code evaluates stress intensity factor at the crack tip for a cracked plate under plane stress. Parametric simulations consider the plate, actuator, and adhesive bond characteristics. Machine learning is then used to determine how these parameters influence repair performance, identifying the most influential factors for improving actuator quality while reducing time and cost.","A. Anjum et alii, Frattura edIntegrità Strutturale, 66 (2023) 112-126; DOI: 10.3221/IGF-ESIS.66.06  \nAnalysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach  \nAsraar Anjum  \nDepartment of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50725 Kuala Lumpur, Malaysia  \n[asraar.anjum@live.iium.edu.my](asraar.anjum@live.iium.edu.my)  \nAbdul Aabid  \nDepartment of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia  \n[aaabid@psu.edu.sa](aaabid@psu.edu.sa)  \nMeftah Hrairi*  \nDepartment of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50725 Kuala Lumpur, Malaysia  \n[meftah@iium.edu.my](meftah@iium.edu.my)  \n\n| ABSTRACT. In recent studies, piezoelectric actuators have been recognized asa practical and effective material for repairing cracks in thin-walled structures, such as plates that are adhesively bonded with piezoelectric patches due to their electromechanical effects. In this study, we used the finite element method through the ANSYS commercial code to determine the stress intensity factor (SIF) at the crack tip of a cracked plate bonded with a piezoelectric actuator under a plane stress model. By running various simulations, we were able to examine the impact of different aspects that affect this component, such as the size and characteristics of the plate, actuator, and adhesive bond. To optimize performance, we utilized machine learning algorithms to examine how these characteristics affect the repair process. This study represents the first-time machine learning has been used to examine bonded PZT actuators in damaged structures, and we found that it had a significant impact on the current problem. As a result, we were able to determine which of these parameters were most helpful in achieving our goal and which ones should be adjusted to improve the actuator's quality and reduce significant time and costs.\u003Cbr>KEYWORDS. Damaged structure, Piezoelectric actuators, Finite element method, Machine learning. | \u003Cbr>Citation: Anjum, A., Aabid, A., Hrairi, M., Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach, Frattura ed Integrità Strutturale, 66 (2023) 112-126.\u003Cbr>Received: 30.04.2023\u003Cbr>Accepted: 02.08.2023\u003Cbr>Online first: 05.08.2023\u003Cbr>Published: 01.10.2023\u003Cbr>Copyright: © 2023 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |\n| --- | --- |\n\nA. Anjum et alii, Frattura edIntegrità Strutturale, 66 (2023) 112-126; DOI: 10.3221/IGF-ESIS.66.06  \nINTRODUCTION  \nM  \nany structural health monitoring researchers are interested in this topic because there is a rising need for an efficient, affordable, and trustworthy monitoring system to guarantee the functioning and safety of such structures [1–4] . Due to cyclic stresses and a corrosive operating environment, aircraft are susceptible to fracture  \nover time. For example, fatigue cracks can form in corroded rivet holes and must be discovered and corrected before they cause catastrophic failure [5] .  \nIn early studies, damaged structures were studied by determining the fracture parameter such as stress intensity and stress concentration factor by using mathematical modelling [6] and the finite element method [7] . Later on, the same approach was used to compute SIF with bonded laminate structures [8,9] . After successfully computing the fracture parameter this continued with changing the parameter [10], damage propagation mode [11,12], and single/double-sided composite effects [13] . As an active repair method, the PZT was used to repair a notched beam under dynamic loading conditions by the electromechanical","cbCaiqfUid7F9aDH","https://ap.wps.com/l/cbCaiqfUid7F9aDH","pdf",1861338,1,15,"English","en",105,"# Abstract\n# Introduction\n## Structural health monitoring needs\n## Fracture parameter modeling and finite elements\n## PZT-based repair and numerical/analytical approaches\n## Recent machine learning and deep learning applications to crack/damage detection","[{\"question\":\"How is stress intensity factor at the crack tip evaluated in the study?\",\"answer\":\"The stress intensity factor is computed using finite element analysis in ANSYS under a plane stress model for a cracked plate bonded with a piezoelectric actuator.\"},{\"question\":\"Which parameters are investigated to understand their effect on the repair process?\",\"answer\":\"Simulations examine how plate, actuator, and adhesive bond size and characteristics influence the repair response and resulting stress intensity factor.\"},{\"question\":\"What role does machine learning play in optimizing the damage control approach?\",\"answer\":\"Machine learning algorithms are used to assess how the identified characteristics affect repair performance, helping determine which parameters are most helpful and which should be adjusted to improve actuator quality and reduce cost and time.\"}]","Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach | 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