Optimum sensor position for inverse load recovery on a composite plate

Cameron W. Coates, Ray R. Hashemi, Darryl W. Daniell

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

An inverse method using strain values obtained at finite locations is used to predict coefficients of an unknown continuous static load function applied to a composite sandwich plate. The strain values obtained from a Finite Element Model (FEM) are used to simulate strain sensor readings. A Back-Propagation Neural Network (BPNN) is developed in order to predict optimum strain sensor locations necessary for accurate load function coefficient recovery using the inverse method. Coefficient values output from the inverse method, as well as material, and geometrical properties are used as input into the BPNN. The BPNN utilizes a back propagation algorithm that incorporates a linear transfer function. The BPNN developed predicts locations for sensor placement such that load function coefficients are recovered by the inverse method with good accuracy.

Original languageEnglish
Title of host publicationAdvances and Trends in Engineering Materials and their Applications - Proceedings of AES-ATEMA 1st International Conference
Pages485-492
Number of pages8
StatePublished - 2007
Event1st International Conference on Advances and Trends in Engineering Materials and their Applications, AES-ATEMA'2007 - Montreal, QC, Canada
Duration: Aug 6 2007Aug 10 2007

Publication series

NameAES-ATEMA International Conference Series - Advances and Trends in Engineering Materials and their Applications
ISSN (Print)1924-3642

Conference

Conference1st International Conference on Advances and Trends in Engineering Materials and their Applications, AES-ATEMA'2007
Country/TerritoryCanada
CityMontreal, QC
Period08/6/0708/10/07

Keywords

  • Finite element
  • Inverse
  • Neural network
  • Sandwich plate
  • Strain

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