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Parametric Design and Experimental Validation of Conjugate Stress Sensors for Structural Health Monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Parametric Design and Experimental Validation of Conjugate Stress Sensors for Structural Health Monitoring./
作者:	Kordell, Jonathan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	126 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28652360
ISBN:	9798460433537

Parametric Design and Experimental Validation of Conjugate Stress Sensors for Structural Health Monitoring.
Kordell, Jonathan.

Parametric Design and Experimental Validation of Conjugate Stress Sensors for Structural Health Monitoring. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 126 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2021.

This item must not be sold to any third party vendors.

In this dissertation, conjugate stress (CS) sensing is advanced through a parametric evaluation of a surface-mounted design and through experimental validation in monotonic and cyclic tensile tests. The CS sensing concept uses a pair of sensors of significantly different mechanical stiffness for direct query of the instantaneous local stress-strain relationship in the host structure, thus offering measurement of important health indicators such as stiffness (modulus), yield strength, strain hardening, and cyclic hysteresis. In this study, surface-mounted CS sensor designs are parametrically evaluated with finite element modeling, with respect to the sensors' location, thickness, and modulus and the external loading state. An analytic pin-force model is developed to infer the host structure's stress-strain state, based on the strain outputs of the CS sensor-pair. Two CS sensor designs are fabricated - one employs resistive foil strain gauges and the second employs fiber optic sensors - and paired with the pin-force model for experimental demonstration of the measurement of: (i) stress-strain history of three different isotropic metal bars (aluminum, copper, and steel) as they experience monotonic tensile loads well into plasticity and (ii) stress-strain hysteresis of a steel bar as it is subject to cyclic tensile fatigue. In the cyclic tests, two machine learning algorithms - anomaly detection and neural net classification - are used in conjunction with the estimated host stiffness from the CS sensor and pin force model to predict the onset of damage in the steel beams.

ISBN: 9798460433537Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Conjugate stress sensor

Parametric Design and Experimental Validation of Conjugate Stress Sensors for Structural Health Monitoring.
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300 $a 126 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
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506 $a This item must not be sold to any third party vendors.
520 $a In this dissertation, conjugate stress (CS) sensing is advanced through a parametric evaluation of a surface-mounted design and through experimental validation in monotonic and cyclic tensile tests. The CS sensing concept uses a pair of sensors of significantly different mechanical stiffness for direct query of the instantaneous local stress-strain relationship in the host structure, thus offering measurement of important health indicators such as stiffness (modulus), yield strength, strain hardening, and cyclic hysteresis. In this study, surface-mounted CS sensor designs are parametrically evaluated with finite element modeling, with respect to the sensors' location, thickness, and modulus and the external loading state. An analytic pin-force model is developed to infer the host structure's stress-strain state, based on the strain outputs of the CS sensor-pair. Two CS sensor designs are fabricated - one employs resistive foil strain gauges and the second employs fiber optic sensors - and paired with the pin-force model for experimental demonstration of the measurement of: (i) stress-strain history of three different isotropic metal bars (aluminum, copper, and steel) as they experience monotonic tensile loads well into plasticity and (ii) stress-strain hysteresis of a steel bar as it is subject to cyclic tensile fatigue. In the cyclic tests, two machine learning algorithms - anomaly detection and neural net classification - are used in conjunction with the estimated host stiffness from the CS sensor and pin force model to predict the onset of damage in the steel beams.
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653 $a Fatigue
653 $a Fiber optics
653 $a Prognostic health management
653 $a Structural health monitoring
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