Development and Characterization of Al-E Glass Fiber Composites

Metal Matrix Composites (MMCs) have brought a keen interest in current scenario for potential applications in automotive industries owing to their superior strength to weight ratio.In the present work a modest attempt has been made to develop aluminium (Al) based E-glass fibre composites with an objective to produce economic method of obtaining high strength MMCs. E-Glass fibers are the most widely used glass fibers as reinforcement in composites. E-glass fibers show proficient bulk properties such as high hardness (6000 MPa), high strength ,dimensional stability, resistance to chemical attack .It has density of 2.58 gm/cc, tensile strength of 3500 MPa and Young’s modulus of 85 GPa along with compressive strength of 5000 MPa. The present work investigates the microstructure and hardness of an Al-based MMC reinforced with different (1, 2, 5) vol. % of E-glass fiber developed by powder metallurgy route. The E- glass fiber having different length (2 , 5 and 8 mm) were chosen for each composition. Both unmilled and 20h milled nanostructured Al were used as matrix and mixed with E-glass fiber in different vol. % and compacted under uniaxial load of 222 MPa and sintered at 500oC for 2h in Ar atmosphere. For a period of upto20 h Al was milled in a high energy planetary ball mill in order to develop nanocrystalline Al powder. The milled Al powder was analyzedusing scanning electron microscope (SEM) ,x-ray diffraction (XRD), and high resolution transmission electron microscope (HRTEM). The effect of nanocrystalline Al on densification and sintering was analyzed. The microstructure of the various Al based MMCs were analyzed using SEM and optical microscope. The hardness of the various composites was measured using a Vickers microhardness tester. SEM was used to analyse the fracture surface of the composites. The relative density of sintered samples was calculated from Archimedes’ principl

Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.Comment: 7 pages, presented at the IEEE 20th India Council International Conference (INDICON 2023), Hyderabad, India, December 202