Current Research

updated on January 2023

This work presents a novel artificial neural network (ANN) framework for fiber-reinforced polymer (FRP) composites under fatigue loading, which incorporates dielectric state variables to predict the life (durability) and residual strength (damage tolerance) from real-time acquired dielectric permittivity of the material.

The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging.  The physics behind the hygrothermal aging of the composites has then been interpreted by comparing the model attributes to see which characteristics most strongly influence the predictions.

Fatigue test is an important characterization technique to better understand the service life of composite parts. Typical tension tension fatigue tests require commercial machines which are very costly and related to elevated risk due to the hydraulic components. In this research work, we are developing a multi-mode fatigue testing machine which will be able to carry out two different type of fatigue tests:

End loaded bending fatigue test

Torsion fatigue test

The Primary design phase is completed. Now the assembly and coding part is being developed. 

This machine will cost less than 2000$ and coded in Python, which will ensure its adaptation in small-to-large labs where experimental mechanics is a topic of study.  We believe, upon completing the project it can be published as an open source design soon. 

[more to follow...] 

Studying strain is one of the most important parts in the mechanical testing of materials. A number of techniques are being used to study this particular which includes direct data accumulation from the cross head movement of the testing apparatus, using strain gauges(extensometer, etc.), optical fiber, etc. These techniques are often misleading as they only show global strain instead of the local strain values. The test specimen under load does not develop exact same strain throughout its body. So, the local strain is never equal to the global strain. 

For quantitative in-plane deformation measurement of a planar object surface, 2D Digital Image Correlation (DIC) is a practical and effective tool. This is now widely excepted in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full field strains by doing comparison of the digital images of a test specimen surface captured before and after deformation. This tool is very handy in terms of acquiring accurate local strain data compared to the above mentioned methods.

We have developed a Picamera and Raspberry Pi based system, which can produce local strain field in Real-time and produce accurate results. Not to mention, the overall cost of this setup is below 100$, compared to the professionally available ones which can cost more than 30,000 to 40,000$. The software is developed on Python and is published on github under MIT license. [Link] 

Know more about this: 

https://utaresearch.github.io/RealPi2dDIC/sop.html 

https://utaresearch.github.io/RealPi2dDIC/docs.html 

The machine is a low cost, desktop size, open source, Tensile testing machine, designed for inexpensive high-throughput material testing. The design was done and provided by Creative Machines Lab from Columbia University.  

Here at IPPM…

•Parts were assembled using the directions from the manual

•A Graphical User Interface(GUI) is developed using Python

•The developed program controls MTTM in Displacement Control Mode

Change log v1.0.2

•Speed, Displacement of Cross-head and force are calibrated

•Two samples can be tested simultaneously

•Data Acquisition feature added

•Real-time Plot feature added

•GUI is upgraded to make it more User-friendly