{"title":"Design and development of Cu-Al-Mn-Ni shape memory alloy coated optical fibre sensor for condition-based monitoring of physical systems","authors":"Karthick Subramaniam, Shalini Singh, Sumeet Raikwar, Ashish Kumar Shukla, Iyamperumal Anand Palani","doi":"10.1049/cim2.12020","DOIUrl":null,"url":null,"abstract":"<p>Online fault detection, isolation and recovery using smart sensors play an important role in intelligent manufacturing system. Fibre optic sensors are very interesting for condition monitoring applications due to the advantage of this technology. Here, the experimental demonstration of Cu-based shape memory alloy (SMA) coated optical fibre for temperature-based sensing applications is reported. The benefit of Cu-based SMA coated optical fibre over conventional metallic coating has been evaluated in the study. For consistent coating, an in situ fixture with a rotary drive setup has been designed and developed. Thermo optic test bench has been developed to study the actuation characteristics of the SMA coated optical fibre for varying current and voltage. Experiments were performed to investigate the light intensity in the SMA coated optical fibre at different actuation conditions. The displacement that takes place in the optical fibre due to the external temperature stimuli will create proportional intensity and wavelength shifts. The maximum average displacement of 4.9 mm has been achieved for Cu-Al-Mn-Ni coated optical fibre. Results show variation in the optical fibre signal due to heating and cooling of the fibre from the applied electrical stimulus on Cu-based SMA coating in the form of austenite to martensite transformation.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 2","pages":"193-202"},"PeriodicalIF":2.5000,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12020","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Online fault detection, isolation and recovery using smart sensors play an important role in intelligent manufacturing system. Fibre optic sensors are very interesting for condition monitoring applications due to the advantage of this technology. Here, the experimental demonstration of Cu-based shape memory alloy (SMA) coated optical fibre for temperature-based sensing applications is reported. The benefit of Cu-based SMA coated optical fibre over conventional metallic coating has been evaluated in the study. For consistent coating, an in situ fixture with a rotary drive setup has been designed and developed. Thermo optic test bench has been developed to study the actuation characteristics of the SMA coated optical fibre for varying current and voltage. Experiments were performed to investigate the light intensity in the SMA coated optical fibre at different actuation conditions. The displacement that takes place in the optical fibre due to the external temperature stimuli will create proportional intensity and wavelength shifts. The maximum average displacement of 4.9 mm has been achieved for Cu-Al-Mn-Ni coated optical fibre. Results show variation in the optical fibre signal due to heating and cooling of the fibre from the applied electrical stimulus on Cu-based SMA coating in the form of austenite to martensite transformation.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).