Real Time Defect Identification using Advanced Artificial Intelligence based Material Strain Sensors for Environmental Safety
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Abstract
Advanced materials find applications in various fields of engineering and medicine. Many advanced materials are synthesized characterised to design sensors for various application including environmental protection. This work explores the possibility of real time defect identification using advanced materials such as graphene composite based sensor for Environment safety. Advanced materials like Graphene composite based sensor were prepared using solvent casting technique. Possibility of using graphene-PVDF based sensors as non- destructive probes to identify surface defects of materials were evaluated by probing on materials with pre-machined defects. Defects were induced artificially in different materials such as (SS316 LN stainless steel) flat plate and cylindrical disc plate. Defects vary both in size and depth. Nanocomposite films were cut in to small pieces of equal dimension and pasted over the various defects. The variation in size and depth of the defects is measured in terms of variation in resistance and suitably calibration graphs were obtained. Using advanced materials for environmental protection, Industry environment safety can be monitored and essential measures can be carried out. This work also explores the change in absorption property of carbon based nanocomposite films on addition of different weight percentage of advanced material based fillers such as Multiwalled carbon nanotubes and graphene. Filler weight percentage increases in matrix which is indicated by red shift in the absorption spectra.
In this research work the use of advanced materials for environmental protection in the Metallurgical Industry is explored . Development of advanced functional materials were carried out . Characterization of these advanced material were also carried out . Sensitivity of these advanced material based sensors were analyzed and calibarated. By using these advanced material based senors catastrophic disorders can be avoided and environmental safety is ensured.
The non-destructive evaluation (NDE) method that is being presented uses a Carbon Nanotube (CNT) and graphene-based sensor to identify defects in pre-machined stainless steel plates is innovative and straightforward. Utilizing nano composites is meant to combine the advantageous qualities of the filler and matrix. It was decided to use PVDF, a piezoelectric polymer, because of its benefits including low cost, versatility, and quick response time. A small amount (weight%) of MWCNTs and graphene are employed as filler to improve the material's shortcomings, such as delayed electrical response, and to make it behave as a piezoresistive and conductive material.
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