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Analytical Modeling of Embedded Load Sensing Using Liquid-Filled Capillaries Integrated by Metal Additive Manufacturing
- Source :
- IEEE Sensors Journal. 19:9447-9455
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Additive manufacturing (AM) offers new manufacturing solutions for the integration of smart functionalities in engineering structures. In this paper, an analytical model is presented for an embedded load sensing element based on a liquid-filled capillary. During the additive manufacturing process, the capillary is integrated in the region where the strain is to be determined. The embedded capillary deforms as the structure deforms under an applied load, as such altering the pressure inside the capillary. The monitoring of the capillary pressure allows monitoring the loads and thus usage of the component. This paper presents a model describing the behavior of the sensing element under uniform tensile stress. The sensitivity of the load sensing element per unit longitudinal strain depends on the bulk modulus of the liquid inside the capillary and the Poisson coefficient of the surrounding material. The current work further compares the analytical model against static tension-compression tests of powder bed fused stainless steel (AISI 316L) test specimen with an integrated capillary filled with a liquid (water). Similarly, the validation of the model is then checked against a dynamic four-point bending test on a Ti-6Al-4V specimen produced by powder bed fusion.
- Subjects :
- Work (thermodynamics)
Bulk modulus
Capillary pressure
Materials science
Additive manufacturing
Capillary action
010401 analytical chemistry
Bending
embedded load sensing
01 natural sciences
0104 chemical sciences
usage monitoring
Structural Health Monitoring
Structural health monitoring
Sensitivity (control systems)
Electrical and Electronic Engineering
Composite material
Current (fluid)
effective Structural Health Monitoring
Instrumentation
Subjects
Details
- ISSN :
- 23799153 and 1530437X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Sensors Journal
- Accession number :
- edsair.doi.dedup.....521f66617c99f6e14db80991c4cf3f06
- Full Text :
- https://doi.org/10.1109/jsen.2019.2925206