EAS Doctoral Defense by Mazharul Islam Lincon
When: Tuesday,
January 30, 2024
3:00 PM
-
5:00 PM
Where: > See description for location
Description: EAS Doctoral Defense by Mazharul Islam Lincon
DATE: January 30, 2024
TIME: 3:00 p.m.
LOCATION: Library, Room 314
TOPIC: Mechanical response and damage monitoring in hybrid composites under extreme loading conditions
ABSTRACT:
The rising interest in composite materials within aerospace, defense, and automotive industries has prompted a thorough investigation of their material behavior under different extreme loading conditions. Understanding their damage evolution through in-built sensing techniques is crucial for continuous structural health monitoring, particularly in scenarios involving extreme loads. Therefore, a comprehensive investigation is performed to determine the piezo-resistance response for damage monitoring in glass/carbon intra-ply, inter-ply, and additively manufactured hybrid thermoplastic composites under various extreme loading conditions (shear loading, mode-I fracture loading, shock loading, and projectile impact loading). Carbon yarns in intra-ply fabric and carbon nanotubes (CNTs) embedded in both inter-ply and additively manufactured composites are used to generate an electrically conductive network within these composites. The change in the electrical network of the composites under above extreme loadsis determined using modified four probe measurement techniques. High-speed imaging is used to capture real-time deformation and crack dynamics, while scanning electron microscopy imaging is utilized to analyze the fracture surfaces and later these two are correlated with the mechanical and electrical responses. The orientation of glass and carbon fibers in intra-ply and inter-ply composites considerably influenced the strength/fracture toughness and piezo-resistance sensitivity for all extreme loading conditions. Results show that the modified four probe measurement technique can predict the damage initiation and propagation accurately. In addition to experimental studies, Deep Learning techniques are employed to predict the piezo-resistance response of hybrid composites under dynamic shear and fracture loading conditions. Four different models are identified whose predictions match well with experimental responses. This comprehensive study not only sheds light on how hybrid composites respond to various extreme loading conditions but also offers robust damage-sensing capabilities for their structural health monitoring.
ADVISOR:
Dr. Vijaya Chalivendra, Professor, Department of Mechanical Engineering, UMassD
COMMITTEE MEMBERS:
Dr. Jianyi Jay Wang, Professor, Department of Physics, UMassD
Dr. Jun Li, Assistant Professor, Department of Mechanical Engineering, UMassD
Dr. Caiwei Shen, Assistant Professor, Department of Mechanical Engineering, UMassD
Dr. Helio Matos, Assistant Professor, Department of Mechanical, Industrial, and Systems Engineering, URI
Open to the public. All EAS students are encouraged to attend.
DATE: January 30, 2024
TIME: 3:00 p.m.
LOCATION: Library, Room 314
TOPIC: Mechanical response and damage monitoring in hybrid composites under extreme loading conditions
ABSTRACT:
The rising interest in composite materials within aerospace, defense, and automotive industries has prompted a thorough investigation of their material behavior under different extreme loading conditions. Understanding their damage evolution through in-built sensing techniques is crucial for continuous structural health monitoring, particularly in scenarios involving extreme loads. Therefore, a comprehensive investigation is performed to determine the piezo-resistance response for damage monitoring in glass/carbon intra-ply, inter-ply, and additively manufactured hybrid thermoplastic composites under various extreme loading conditions (shear loading, mode-I fracture loading, shock loading, and projectile impact loading). Carbon yarns in intra-ply fabric and carbon nanotubes (CNTs) embedded in both inter-ply and additively manufactured composites are used to generate an electrically conductive network within these composites. The change in the electrical network of the composites under above extreme loadsis determined using modified four probe measurement techniques. High-speed imaging is used to capture real-time deformation and crack dynamics, while scanning electron microscopy imaging is utilized to analyze the fracture surfaces and later these two are correlated with the mechanical and electrical responses. The orientation of glass and carbon fibers in intra-ply and inter-ply composites considerably influenced the strength/fracture toughness and piezo-resistance sensitivity for all extreme loading conditions. Results show that the modified four probe measurement technique can predict the damage initiation and propagation accurately. In addition to experimental studies, Deep Learning techniques are employed to predict the piezo-resistance response of hybrid composites under dynamic shear and fracture loading conditions. Four different models are identified whose predictions match well with experimental responses. This comprehensive study not only sheds light on how hybrid composites respond to various extreme loading conditions but also offers robust damage-sensing capabilities for their structural health monitoring.
ADVISOR:
Dr. Vijaya Chalivendra, Professor, Department of Mechanical Engineering, UMassD
COMMITTEE MEMBERS:
Dr. Jianyi Jay Wang, Professor, Department of Physics, UMassD
Dr. Jun Li, Assistant Professor, Department of Mechanical Engineering, UMassD
Dr. Caiwei Shen, Assistant Professor, Department of Mechanical Engineering, UMassD
Dr. Helio Matos, Assistant Professor, Department of Mechanical, Industrial, and Systems Engineering, URI
Open to the public. All EAS students are encouraged to attend.
Contact: Engineering and Applied Sciences
Topical Areas: Faculty, Students, Students, Graduate, Students, Undergraduate, Bioengineering, Civil and Environmental Engineering, College of Engineering, Computer and Information Science, Co-op Program, Electrical and Computer Engineering, Mechanical Engineering, Physics