University of Texas at Arlington Research Institute (UTARI)
http://hdl.handle.net/10106/27239
2024-03-28T22:53:20ZCarbon Fibers Recycling from Degraded Prepregs and Mechanical Properties or Recycled Composite
http://hdl.handle.net/10106/31204
Carbon Fibers Recycling from Degraded Prepregs and Mechanical Properties or Recycled Composite
Rabby, Monjur Morshed; Rahman, Minhazur; Das, Partha Pratim; Vadlamudi, Vamsee; Raihan, Rassel
The size of the global composites market is anticipated to grow more than previously. Due to the rapidly rising volume of CFRP production, the waste from this material poses numerous problems and has significantly increased the socio-technological pressure to find sustainable composite recycling solutions. The problem is that recycling the composite part is challenging once its service life has expired. The same problem is true for the raw materials (prepregs) used in composite manufacturing. When the prepreg out-life/shelf-life is over, prepregs are abandoned, resulting in a loss of millions of dollars and an adverse environmental effect. In this study, the prepreg matrix and fiber were separated by a chemical process using acetone as the primary solvent and other oxidants as a secondary treatment. The retrieved fibers were analyzed for surface morphologies and functional groups on the surface and compared with the fiber recovered using the pyrolysis process. Due to the loss of the sizing agent, plasma treatment has been performed to increase the wettability and adhesion between fiber and matrix. This recycled fiber is then used in manufacturing composite panels via the Vacuum Assisted Resin Transfer Molding (VARTM) process. The mechanical properties of the recovered fiber have been studied to ensure that it can be repurposed for other applications. The proposed method can be used to recover carbon fiber, and then the fiber can be used to reinforce the polymer matrix, reducing sociotechnical pressure while remaining cost-effective and environmentally friendly.
2023-04-18T00:00:00ZArtificial Intelligence Assisted Residual Strength and Life Prediction of Fiber Reinforced Polymer Composites
http://hdl.handle.net/10106/31086
Artificial Intelligence Assisted Residual Strength and Life Prediction of Fiber Reinforced Polymer Composites
Das, Partha Pratim; Elenchezhian, Muthu Ram Prabhu; Vadlamudi, Vamsee; Raihan, Rassel
With the increased use of composite materials, researchers have developed many approaches for structural and prognostic health monitoring. Broadband Dielectric Spectroscopy (BbDS)/Impedance Spectroscopy (IS) is a state-of-the-art technology that can be used to identify and monitor the minute changes in damage initiation, accumulation, interactions, and the degree of damage in a composite under static and dynamic loading. 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 findings of this study indicate that this robust ANN-based prognostic framework can be implemented in FRP composite structures, thereby assisting in preventing unforeseeable failure.
2023-01-19T00:00:00ZMechanical and Dielectric Modeling of Adhesive Bonded Fiber Reinforced Composite Single Lap Joints
http://hdl.handle.net/10106/31000
Mechanical and Dielectric Modeling of Adhesive Bonded Fiber Reinforced Composite Single Lap Joints
Rahman, Minhazur; Vadlamudi, Vamsee; Raihan, Rassel
**Please note that the full text is embargoed** ABSTRACT: Fiber Reinforced Polymer (FRP) composites have long since been dominating the structural materials industry due to its superior specific strength and its ability to be designed for desirable mechanical and electrical properties. Using this formidable class of materials as structural components lead to the need of joining and bonding them. Adhesive bonding of composite materials are preferred over conventional mechanical fasteners, rivets, welding etc. as these tend to weaken the structure by introducing additional sources of stress concentrations. However, the complexity from the anisotropy of FRPs and the added convolution of anisotropic (composite adherend) and isotropic (adhesive) interfaces poses significant challenge to properly evaluate and model the adhesive bonded joints of composites. Significant experimental investigations have revealed correlations of material state degradation with the dielectric state variable defined as Dielectric Relaxation Strength (DRS). Due to the lack of appropriately defined physics in commercial simulation software packages, modeling such behaviors were challenging. In this paper a phenomenological modeling approach is taken to simulate the evolution of dielectric properties with the degradation of materials state. Broadband Dielectric Spectroscopy (BbDS) tests were carried out while applying tension on the
specimen. The experiment allowed for an in-situ measurement of dielectric property evolution with material state degradation. The experimental data obtained were used to formulate equations relating material’s mechanical properties with dielectric variables such as real and imaginary permittivity. These equations were inserted into
the material model as constitutive equations and the evolution of dielectric properties with material degradation were simulated using an 1D Finite Element Analysis (FEA) model. [ https://doi.org/10.12783/asc37/36462]
RAHMAN, M., VADLAMUDI, V., & RAIHAN, R. (2022). Mechanical and Dielectric Modeling of Adhesive Bonded Fiber Reinforced Composite Single Lap Joints. In PROCEEDINGS OF THE AMERICAN SOCIETY FOR COMPOSITES-THIRTY-SEVENTH TECHNICAL CONFERENCE.
2022-01-01T00:00:00ZA Data-Driven Mechanical Property Prediction in Epoxy/Glass Fiber Composite
http://hdl.handle.net/10106/30999
A Data-Driven Mechanical Property Prediction in Epoxy/Glass Fiber Composite
Rabby, Monjur Morshed; Vadlamudi, Vamsee; Raihan, Rassel
**Please note that the full text is embargoed** ABSTRACT: Fiber reinforced epoxy based composite materials are widely used in aircraft, marine, and automotive structures to minimize weight and increase performance. Typically, the mechanical performance of a composite part has so far been inspected using destructive testing, which is both expensive and time consuming. The mechanical properties of a composite are influenced by the curing process and its parameters, particularly the mechanical tensile modulus of the polymer, which rises as crosslink density rises and is influenced by curing temperature and holding time. It is, however, critical to manufacture epoxy-based composites with a higher crosslinking density by using the proper and uniform curing temperature. After fabricating the composite part, the most important step is to inspect the mechanical strength. In this study, we have presented a non-destructive quality inspection technique by merging Broadband Dielectric Spectroscopy data with supervised learning algorithms to predict the tensile strength of the cured composite sample based on the sample’s dielectric state variables. Different samples were made using different curing temperature to ensure variability in tensile strength data. A total of 15 features based on the Dielectric characteristics of the sample has been used in the algorithm. In this case, a classifier algorithm has been utilized, which predicts tensile strength based on which subset it belongs to. Overall, an estimation of the mechanical strength can be obtained by measuring the dielectric characteristic using an AC current frequency sweep which is a time-saving and nondestructive procedure and fitting the data in a supervised learning algorithm. [https://doi.org/10.12783/asc37/36459]
RABBY, M. M., VADLAMUDI, V., & RAIHAN, R. (2022). A Data-Driven Mechanical Property Prediction in Epoxy/Glass Fiber Composite. In PROCEEDINGS OF THE AMERICAN SOCIETY FOR COMPOSITES-THIRTY-SEVENTH TECHNICAL CONFERENCE.
2022-01-01T00:00:00Z