[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123105-en":3,"doc-seo-123105-105":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},123105,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","VIBRATION MITIGATION-BASED MACHINE LEARNING-DRIVEN DESIGN OF METASTRUCTURES","Research develops a longitudinally excited metastructure composed of periodically distributed external units, each containing internal oscillators that act as vibration absorbers. The initial design targets vibration attenuation near the first structural resonance with identical uniform absorbers formed by cantilevers in the external components, each ending in a concentrated mass. A machine learning workflow is used to maximize attenuation efficiency around the second resonance while also jointly optimizing first- and second-resonance performance. Redesigned metastructures are fabricated via 3D printing and validated experimentally.","[https://doi.org/10.22190/FUWLEP240929019K](https://doi.org/10.22190/FUWLEP240929019K)  \nOriginal scientific paper  \nVIBRATION MITIGATION-BASED MACHINE LEARNING-DRIVEN DESIGN OF METASTRUCTURES  \nUDC 621.373:004.85:534  \nIvana Kovačić1, Željko Kanović2, Ljiljana Teofanov2, Vladimir Rajs2  \n1University of Novi Sad, Faculty of Technical Sciences, Centre of Excellence for Vibro-Acoustic Systems and Signal Processing CEVAS, Novi Sad, Serbia  \n2 University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia  \nORCID iDs: Ivana Kovačić [https://orcid.org/0000-0002-0433-1953](https://orcid.org/0000-0002-0433-1953)  \nŽeljko Kanović [https://orcid.org/0000-0003-1456-1135](https://orcid.org/0000-0003-1456-1135)  \nLjiljana Teofanov [https://orcid.org/0000-0002-0302-1830](https://orcid.org/0000-0002-0302-1830)  \nVladimir Rajs [https://orcid.org/0000-0003-4357-770X](https://orcid.org/0000-0003-4357-770X)  \nAbstract. This research is concerned with the development of a longitudinally excited metastructure, featuring periodically distributed external units, each equipped with internal oscillators functioning as vibration absorbers. Initially, the metastructure designed for vibration attenuation around the first structural resonance, is characterized by uniformity, with all absorbers being identical and consisting of cantilevers integrated into the external components, each cantilever terminating in a concentrated mass block.  \nThis study employs a machine learning approach to maximize vibration attenuation efficiency around the second resonance, as well as concurrently at the first and second resonant frequencies in two associated optimality criteria related to the width of the attenuation region and the amplitude reduction, respectively. The new metastructures redesigned based on these criteria are fabricated by 3D printing, and their enhanced vibration mitigation capabilities are verified experimentally.  \nKey words: metastructure, vibration mitigation, auxiliary oscillators, machine learning.  \n1. INTRODUCTION  \nThe concept of 'metastructures' has recently emerged in the field of vibration control, evolving from the framework of the concept of metamaterials [1–3] . This approach entails the integration of a series of internal, distributed, and tuned auxiliary oscillators within the external components of a structure, aimed at controlling its vibrational response. Despite its recent introduction, the fundamental principle is rooted in the enhancement of Den  \nReceived September 29, 2024 / Accepted October 10, 2024  \nCorresponding author: Ivana Kovacic  \nUniversity of Novi Sad, Faculty of Technical Sciences, CEVAS, Trg D. Obradovica 6, 21000 Novi Sad, Serbia E-mail: [ivanakov@uns.ac.rs](ivanakov@uns.ac.rs)  \nHartog’s methodology [4], which focuses on managing the response of a main vibrating structure, subjected to external excitation, modelled as a one-degree-of-freedom linear mechanical oscillator. This is achieved by incorporating an auxiliary oscillator that matches the frequencies of the main structure, the external harmonic excitation, and the natural frequency of the auxiliary oscillator itself. Consequently, the auxiliary oscillator must be meticulously designed (tuned) to align with such frequency. As a result, rather than exhibiting resonance characterized by an infinite response at this specific frequency, the undamped main structure, when externally excited, will instead exhibit antiresonance, resulting in a zero-amplitude response [4–6] . The auxiliary oscillator functions as a vibration absorber [4–6], which is, in the context of damping, referred to as a tuned-mass damper [7, 8] .  \nThe application of data science and machine learning (ML) has significantly broadened the scope for numerical development and optimization of engineering structures, including vibration absorbers. Nevertheless, this methodology has not been widely adopted in the design of metastructures that exhibit effective vibration control through speci","cbCaihh7PDVgkvUH","https://ap.wps.com/l/cbCaihh7PDVgkvUH","pdf",487521,1,11,"English","en",105,"# Introduction\n## Background: metastructures and vibration control\n## Tuned auxiliary oscillators and antiresonance\n## Prior work on ML-based metastructure design","[{\"question\":\"What is the proposed metastructure configuration in the study?\",\"answer\":\"It uses periodically distributed external units containing internal oscillators that function as vibration absorbers, enabling vibration attenuation at targeted resonant frequencies.\"},{\"question\":\"How does machine learning contribute to improving vibration mitigation?\",\"answer\":\"The ML approach maximizes attenuation efficiency around the second resonance and simultaneously optimizes performance at the first and second resonant frequencies using criteria related to attenuation bandwidth and amplitude reduction.\"},{\"question\":\"How are the redesigned metastructures validated?\",\"answer\":\"The metastructures are redesigned according to the optimization criteria, fabricated with 3D printing, and then experimentally tested to verify improved vibration mitigation.\"}]","VIBRATION MITIGATION-BASED MACHINE LEARNING-DRIVEN DESIGN OF METASTRUCTURES | 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is the proposed metastructure configuration in the study?","Question",{"text":75,"@type":76},"It uses periodically distributed external units containing internal oscillators that function as vibration absorbers, enabling vibration attenuation at targeted resonant frequencies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning contribute to improving vibration mitigation?",{"text":80,"@type":76},"The ML approach maximizes attenuation efficiency around the second resonance and simultaneously optimizes performance at the first and second resonant frequencies using criteria related to attenuation bandwidth and amplitude reduction.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the redesigned metastructures validated?",{"text":84,"@type":76},"The metastructures are redesigned according to the optimization criteria, fabricated with 3D printing, and then experimentally tested to verify improved vibration 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